.gitignore

@@ -3,6 +3,9 @@
 ##
 ## Get latest from https://github.com/github/gitignore/blob/master/VisualStudio.gitignore
 
+# Learned chess engine weights (runtime training output, not source)
+chess-data/
+
 # User-specific files
 *.rsuser
 *.suo

JoshHeaps.Net/Controllers/ChessController.cs

@@ -15,6 +15,7 @@ public class ChessController(
     IBackgroundTaskQueue queue,
     IChessEngineFactory engineFactory,
     IComputerMoveOrchestrator orchestrator,
+    ILearnedWeightsStore weightsStore,
     IHubContext<ChessHub> chessHub) : ControllerBase
 {
     /// <summary>
@@ -30,6 +31,10 @@ public class ChessController(
     private static readonly TimeSpan _selfPlayMoveDelay = TimeSpan.FromSeconds(1);
     private static readonly TimeSpan _selfPlayResultTimeout = TimeSpan.FromSeconds(30);
 
+    // Plies of random legal moves at the start of a training game, so self-play and
+    // engine-vs-engine games explore different lines instead of replaying one game.
+    private const int _openingRandomPlies = 4;
+
     /// <summary>
     /// Create a new chess game and store it in-memory.
     /// </summary>
@@ -52,22 +57,24 @@ public class ChessController(
             _ => Random.Shared.Next(2) == 0,
         };
 
-        gameState.Computer = engineFactory.Create(difficulty);
+        var computer = engineFactory.Create(difficulty);
 
         if (isWhite)
         {
             gameState.WhitePlayerId = playerId;
             gameState.BlackPlayerId = computerId;
+            gameState.BlackComputer = computer;
         }
         else
         {
             gameState.WhitePlayerId = computerId;
             gameState.BlackPlayerId = playerId;
+            gameState.WhiteComputer = computer;
             queue.Queue(async () =>
             {
                 // Give user's browser time to connect to signalR and such.
                 await Task.Delay(TimeSpan.FromSeconds(1));
-                await orchestrator.PlayAsync(gameState, gameState.Computer!);
+                await orchestrator.PlayAsync(gameState);
             });
         }
 
@@ -82,13 +89,22 @@ public class ChessController(
     }
 
     /// <summary>
-    /// Create a game the computer plays against itself and auto-play it move by move,
+    /// Create a computer-vs-computer game and auto-play it move by move, broadcasting each
-    /// broadcasting each move so it can be watched on the spectator page.
+    /// move so it can be watched on the spectator page. Each side's engine and skill can be
+    /// chosen independently; when the learned engine plays, the game also trains it.
     /// </summary>
     [HttpGet("watch/cpu")]
     [HttpGet("watch/cpu/{difficulty}")]
-    public ActionResult CreateSelfPlayGame(int difficulty = 4)
+    public ActionResult CreateSelfPlayGame(
+        int difficulty = 4,
+        string whiteEngine = "custom",
+        string blackEngine = "custom",
+        int? whiteSkill = null,
+        int? blackSkill = null)
     {
+        var whiteKind = ParseEngineKind(whiteEngine);
+        var blackKind = ParseEngineKind(blackEngine);
+
         var gameState = chessService.CreateNewGame();
         _games[gameState.GameId] = gameState;
 
@@ -98,7 +114,14 @@ public class ChessController(
         gameState.BlackJoined = true;
         gameState.WhitePlayerId = Guid.NewGuid();
         gameState.BlackPlayerId = Guid.NewGuid();
-        gameState.Computer = engineFactory.Create(difficulty);
+        gameState.WhiteEngineKind = whiteKind;
+        gameState.BlackEngineKind = blackKind;
+        gameState.WhiteComputer = engineFactory.Create(whiteSkill ?? difficulty, whiteKind);
+        gameState.BlackComputer = engineFactory.Create(blackSkill ?? difficulty, blackKind);
+
+        // When the learned engine is playing, attach a trainer so the outcome can train it.
+        if (whiteKind == ChessEngineKind.CustomLearned || blackKind == ChessEngineKind.CustomLearned)
+            gameState.Trainer = weightsStore.CreateTrainer();
 
         ScheduleRemoveGame(gameState.GameId, _computerGameTimeout);
         StartSelfPlay(gameState);
@@ -106,6 +129,13 @@ public class ChessController(
         return Ok(new { gameState.GameId });
     }
 
+    private static ChessEngineKind ParseEngineKind(string value) => value.ToLowerInvariant() switch
+    {
+        "stockfish" => ChessEngineKind.Stockfish,
+        "customlearned" or "learned" => ChessEngineKind.CustomLearned,
+        _ => ChessEngineKind.Custom
+    };
+
     /// <summary>
     /// Joins the "pool" of chess players.
     /// Test code expects to receive a GUID for the player
@@ -163,6 +193,8 @@ public class ChessController(
                 g.GameId,
                 g.IsVsComputer,
                 g.IsComputerVsComputer,
+                WhiteEngine = g.WhiteEngineKind.ToString(),
+                BlackEngine = g.BlackEngineKind.ToString(),
                 CurrentPlayer = g.CurrentPlayer.ToString(),
                 MoveCount = g.MoveHistory.Count,
                 g.IsCheck
@@ -172,6 +204,26 @@ public class ChessController(
         return Ok(activeGames);
     }
 
+    /// <summary>
+    /// The learned engine's piece-square bonus table, for the weights-visualization page:
+    /// one 64-entry array per piece type (Pawn..King), white-relative (A1=0 .. H8=63).
+    /// </summary>
+    [HttpGet("weights")]
+    public ActionResult GetLearnedWeights()
+    {
+        var names = new[] { "Pawn", "Knight", "Bishop", "Rook", "Queen", "King" };
+        var featureNames = new[] { "Mobility N", "Mobility B", "Mobility R", "Mobility Q", "Passed", "Isolated", "Doubled", "King safety" };
+
+        var snapshot = weightsStore.Snapshot();
+
+        return Ok(new
+        {
+            mg = snapshot.Mg.Select((squares, i) => new { name = names[i], squares }),
+            eg = snapshot.Eg.Select((squares, i) => new { name = names[i], squares }),
+            features = snapshot.Features.Select((value, i) => new { name = featureNames[i], value })
+        });
+    }
+
     /// <summary>
     /// Get the state of an existing game by ID.
     /// </summary>
@@ -232,8 +284,12 @@ public class ChessController(
         await chessHub.Clients.Group(gameState.GameId.ToString())
             .SendAsync("ReceiveMoveUpdate", gameState.GameId.ToString(), moveDto, result, state);
 
-        if (!isGameOver && gameState.IsVsComputer && gameState.Computer is not null)
+        var sideToMoveEngine = gameState.CurrentPlayer == PieceColor.White
-            queue.Queue(() => orchestrator.PlayAsync(gameState, gameState.Computer!));
+            ? gameState.WhiteComputer
+            : gameState.BlackComputer;
+
+        if (!isGameOver && gameState.IsVsComputer && sideToMoveEngine is not null)
+            queue.Queue(() => orchestrator.PlayAsync(gameState));
 
         return Ok(new { result, state });
     }
@@ -314,8 +370,9 @@ public class ChessController(
     }
 
     /// <summary>
-    /// Drives a computer-vs-computer game: keeps asking the engine for the side-to-move's
+    /// Drives a computer-vs-computer game: keeps asking the side-to-move's engine for its
-    /// move (which applies and broadcasts it) until the game ends or is removed.
+    /// move (which applies and broadcasts it) until the game ends or is removed. Training
+    /// games get a randomized opening and feed their result back into the learned weights.
     /// </summary>
     private void StartSelfPlay(GameState gameState)
     {
@@ -324,15 +381,19 @@ public class ChessController(
             // Give spectators a moment to join the SignalR group before the first move.
             await Task.Delay(TimeSpan.FromSeconds(1));
 
-            while (_games.ContainsKey(gameState.GameId)
+            // Training games open with random moves so they don't replay the same line.
-                && !gameState.IsCheckmate
+            if (gameState.Trainer != nint.Zero)
-                && !gameState.IsStalemate
+                for (int i = 0; i < _openingRandomPlies && _games.ContainsKey(gameState.GameId) && !IsGameOver(gameState); i++)
-                && !gameState.IsThreefoldRepetition
+                {
-                && !gameState.IsForfeited)
+                    await orchestrator.PlayRandomMoveAsync(gameState);
+                    await Task.Delay(_selfPlayMoveDelay);
+                }
+
+            while (_games.ContainsKey(gameState.GameId) && !IsGameOver(gameState))
             {
                 try
                 {
-                    await orchestrator.PlayAsync(gameState, gameState.Computer!);
+                    await orchestrator.PlayAsync(gameState);
                 }
                 catch (Exception ex)
                 {
@@ -343,12 +404,100 @@ public class ChessController(
                 await Task.Delay(_selfPlayMoveDelay);
             }
 
+            ApplyLearning(gameState);
+
             // Leave the finished game in place briefly so spectators can see the result.
             if (_games.ContainsKey(gameState.GameId))
                 ScheduleRemoveGame(gameState.GameId, _selfPlayResultTimeout);
         });
     }
 
+    private static bool IsGameOver(GameState gameState) =>
+        gameState.IsCheckmate || gameState.IsStalemate || gameState.IsThreefoldRepetition || gameState.IsForfeited;
+
+    /// <summary>
+    /// Feeds a finished training game's result into the learned weights, then frees the
+    /// trainer. Both sides teach the table — the winner's squares/features up, the loser's
+    /// down. A checkmate is a full-strength result; a material-imbalance draw is a half-
+    /// strength win for the lower-material side (holding a draw while down material is a
+    /// success; only drawing while up is a failure). A balanced draw, forfeit, or unfinished
+    /// game teaches nothing (but the trainer is still freed).
+    /// </summary>
+    private void ApplyLearning(GameState gameState)
+    {
+        if (gameState.Trainer == nint.Zero)
+            return;
+
+        if (TryDetermineOutcome(gameState, out var winner, out var weight))
+            weightsStore.ApplyResult(gameState.Trainer, winner, weight);
+
+        weightsStore.DestroyTrainer(gameState.Trainer);
+        gameState.Trainer = nint.Zero;
+    }
+
+    /// <summary>
+    /// Determines the trainable outcome of a finished game: the winning color and the reward
+    /// weight. Returns false when the game teaches nothing (balanced draw, forfeit, unfinished).
+    /// </summary>
+    private static bool TryDetermineOutcome(GameState gameState, out PieceColor winner, out double weight)
+    {
+        winner = PieceColor.White;
+        weight = 1.0;
+
+        if (gameState.IsCheckmate)
+        {
+            // The side to move is the mated one, so the winner is the other color.
+            winner = gameState.CurrentPlayer == PieceColor.White ? PieceColor.Black : PieceColor.White;
+            return true;
+        }
+
+        if (gameState.IsStalemate || gameState.IsThreefoldRepetition)
+        {
+            var (white, black) = MaterialCounts(gameState);
+
+            if (white == black)
+                return false;                 // a balanced draw carries no signal
+
+            winner = white < black ? PieceColor.White : PieceColor.Black;
+            weight = 0.5;
+            return true;
+        }
+
+        return false;                         // forfeit / unfinished
+    }
+
+    /// <summary>Total non-king material per side (P=1, N=B=3, R=5, Q=9), for draw adjudication.</summary>
+    private static (int white, int black) MaterialCounts(GameState gameState)
+    {
+        int white = 0, black = 0;
+
+        for (int row = 0; row < 8; row++)
+            for (int col = 0; col < 8; col++)
+            {
+                var piece = gameState.Board[row, col];
+
+                if (piece is null)
+                    continue;
+
+                int value = piece.Type switch
+                {
+                    PieceType.Pawn => 1,
+                    PieceType.Knight => 3,
+                    PieceType.Bishop => 3,
+                    PieceType.Rook => 5,
+                    PieceType.Queen => 9,
+                    _ => 0
+                };
+
+                if (piece.Color == PieceColor.White)
+                    white += value;
+                else
+                    black += value;
+            }
+
+        return (white, black);
+    }
+
     private static void ScheduleRemoveGame(Guid id, TimeSpan delay)
     {
         if (_gameRemovalCancellationTokens.TryRemove(id, out var oldCts))
@@ -366,8 +515,21 @@ public class ChessController(
             {
                 await Task.Delay(delay, cts.Token);
 
-                if (_games.TryGetValue(id, out var game) && game.Computer is not null)
+                if (_games.TryGetValue(id, out var game))
-                    await game.Computer.DisposeAsync();
+                {
+                    if (game.WhiteComputer is not null)
+                        await game.WhiteComputer.DisposeAsync();
+                    if (game.BlackComputer is not null)
+                        await game.BlackComputer.DisposeAsync();
+
+                    // Free the trainer if the game never reached ApplyLearning (e.g. timed out).
+                    // The native ABI is shared via CustomChessEngine's import resolver.
+                    if (game.Trainer != nint.Zero)
+                    {
+                        CustomChessEngine.NativeMethods.trainer_destroy(game.Trainer);
+                        game.Trainer = nint.Zero;
+                    }
+                }
 
                 _games.Remove(id, out _);
             }

JoshHeaps.Net/Models/GameState.cs

@@ -1,4 +1,5 @@
-using JoshHeaps.Net.Services.Interfaces;
+using JoshHeaps.Net.Services.Implementations;
+using JoshHeaps.Net.Services.Interfaces;
 
 namespace JoshHeaps.Net.Models;
 
@@ -59,7 +60,20 @@ public class GameState
     public bool IsVsComputer { get; set; } = false;
     public bool IsComputerVsComputer { get; set; } = false;
 
-    public IChessEngine? Computer { get; set; }
+    // The engine playing each side (null for a human). In a human-vs-computer game only the
+    // computer's side is set; the orchestrator picks the engine for whoever is to move.
+    public IChessEngine? WhiteComputer { get; set; }
+    public IChessEngine? BlackComputer { get; set; }
+
+    // Which engine implementation each side uses — lets game-over handling know which side(s)
+    // are the learning engine, and lets spectators see who is playing.
+    public ChessEngineKind WhiteEngineKind { get; set; }
+    public ChessEngineKind BlackEngineKind { get; set; }
+
+    // Native per-game training accumulator (nint.Zero when this game isn't training the
+    // learned engine). The engine records each played position into it and applies the
+    // result on game over.
+    public nint Trainer { get; set; }
 
     // optional: convenience
     public bool IsOpen => !WhiteJoined || !BlackJoined;

JoshHeaps.Net/Models/LearnedWeightsSnapshot.cs

@@ -0,0 +1,9 @@
+namespace JoshHeaps.Net.Models;
+
+/// <summary>
+/// A copy of the learned engine's weights for display: midgame and endgame piece-square
+/// tables (one 64-entry array per piece, in canonical Pawn..King order, white-relative
+/// A1=0..H8=63) plus the feature weights (mobility N/B/R/Q, passed, isolated, doubled,
+/// king safety).
+/// </summary>
+public sealed record LearnedWeightsSnapshot(int[][] Mg, int[][] Eg, int[] Features);

JoshHeaps.Net/Pages/Watch.cshtml

@@ -9,16 +9,38 @@
     <h1>Live Chess</h1>
     <p id="watchStatus">Loading games…</p>
     <div id="watchControls">
-        <label for="cpuDifficulty">Difficulty</label>
+        <fieldset class="enginePicker">
-        <select id="cpuDifficulty">
+            <legend>White</legend>
-            @for (int i = 1; i <= 20; i++)
+            <select id="whiteEngine">
-            {
+                <option value="custom">Custom (v1)</option>
-                <option value="@i" @(i == 4 ? "selected" : "")>@i</option>
+                <option value="customLearned" selected>Custom (Learned)</option>
-            }
+                <option value="stockfish">Stockfish</option>
-        </select>
+            </select>
+            <select id="whiteSkill">
+                @for (int i = 1; i <= 20; i++)
+                {
+                    <option value="@i" @(i == 4 ? "selected" : "")>@i</option>
+                }
+            </select>
+        </fieldset>
+        <fieldset class="enginePicker">
+            <legend>Black</legend>
+            <select id="blackEngine">
+                <option value="custom" selected>Custom (v1)</option>
+                <option value="customLearned">Custom (Learned)</option>
+                <option value="stockfish">Stockfish</option>
+            </select>
+            <select id="blackSkill">
+                @for (int i = 1; i <= 20; i++)
+                {
+                    <option value="@i" @(i == 4 ? "selected" : "")>@i</option>
+                }
+            </select>
+        </fieldset>
         <button id="startCpuVsCpu" onclick="Spectate.startCpuGame()">Watch CPU vs CPU</button>
     </div>
     <a id="backToPlay" href="/chess">← Play a game</a>
+    <a id="viewWeights" href="/weights">View learned weights →</a>
 </div>
 
 <div id="gamesFeed"></div>

JoshHeaps.Net/Pages/Weights.cshtml

@@ -0,0 +1,44 @@
+@page
+@model JoshHeaps.Net.Pages.WeightsModel
+@{
+    Layout = "_Layout";
+    ViewData["Title"] = "Learned Weights";
+}
+
+<div id="weightsHeader">
+    <h1>Learned Piece-Square Weights</h1>
+    <p id="weightsStatus">Where the learned engine thinks each piece belongs.</p>
+    <div id="weightsControls">
+        <div class="heatLegend">
+            <span>low</span>
+            <span class="legendBar"></span>
+            <span>high</span>
+        </div>
+        <button id="refreshWeights" onclick="Weights.refresh()">Refresh</button>
+    </div>
+    <a id="backToWatch" href="/watch">← Watch / train</a>
+</div>
+
+<section class="weightsSection">
+    <h2>Feature weights</h2>
+    <p class="sectionHint">Learned value of each contextual feature (per normalized unit). Mobility is per piece type; passed pawns are endgame-weighted, king safety midgame-weighted.</p>
+    <div id="featureWeights" class="featurePanel"></div>
+</section>
+
+<section class="weightsSection">
+    <h2>Midgame tables</h2>
+    <div id="weightsGridMg" class="weightsGrid"></div>
+</section>
+
+<section class="weightsSection">
+    <h2>Endgame tables</h2>
+    <div id="weightsGridEg" class="weightsGrid"></div>
+</section>
+
+@section Scripts {
+    <script src="~/js/ChessScripts/Weights.js"></script>
+}
+
+@section Styles {
+    <link rel="stylesheet" href="~/css/chess/weights.css?v=@ViewData["cssVersion"]" />
+}

JoshHeaps.Net/Pages/Weights.cshtml.cs

@@ -0,0 +1,11 @@
+using Microsoft.AspNetCore.Mvc.RazorPages;
+
+namespace JoshHeaps.Net.Pages
+{
+    public class WeightsModel : PageModel
+    {
+        public void OnGet()
+        {
+        }
+    }
+}

JoshHeaps.Net/Program.cs

@@ -23,6 +23,7 @@ builder.Services.AddSingleton<IChessService, ChessService>();
 builder.Services.AddSingleton<IBackgroundTaskQueue, BackgroundTaskQueue>();
 
 builder.Services.Configure<ChessEngineOptions>(configuration.GetSection(ChessEngineOptions.SectionName));
+builder.Services.AddSingleton<ILearnedWeightsStore, LearnedWeightsStore>();
 builder.Services.AddSingleton<IChessEngineFactory, ChessEngineFactory>();
 builder.Services.AddSingleton<IComputerMoveOrchestrator, ComputerMoveOrchestrator>();
 

JoshHeaps.Net/Services/Implementations/ChessEngineFactory.cs

@@ -7,7 +7,10 @@ namespace JoshHeaps.Net.Services.Implementations;
 public enum ChessEngineKind
 {
     Stockfish,
-    Custom
+    Custom,
+
+    /// <summary>The custom engine with the reinforcement-learned piece-square evaluation.</summary>
+    CustomLearned
 }
 
 /// <summary>Configuration selecting which <see cref="IChessEngine"/> to use.</summary>
@@ -19,14 +22,19 @@ public sealed class ChessEngineOptions
 }
 
 /// <summary>Creates the configured <see cref="IChessEngine"/> per game.</summary>
-public sealed class ChessEngineFactory(IOptions<ChessEngineOptions> options) : IChessEngineFactory
+public sealed class ChessEngineFactory(
+    IOptions<ChessEngineOptions> options,
+    ILearnedWeightsStore weightsStore) : IChessEngineFactory
 {
     private readonly ChessEngineKind _kind = options.Value.Engine;
 
-    public IChessEngine Create(int skill) => _kind switch
+    public IChessEngine Create(int skill) => Create(skill, _kind);
+
+    public IChessEngine Create(int skill, ChessEngineKind kind) => kind switch
     {
         ChessEngineKind.Custom => new CustomChessEngine(skill),
+        ChessEngineKind.CustomLearned => new CustomChessEngine(skill, EngineVariant.Learned, weightsStore.WeightsFilePath),
         ChessEngineKind.Stockfish => new Stockfish(skill),
-        _ => throw new InvalidOperationException($"Unknown chess engine '{_kind}'.")
+        _ => throw new InvalidOperationException($"Unknown chess engine '{kind}'.")
     };
 }

JoshHeaps.Net/Services/Implementations/ComputerMoveOrchestrator.cs

@@ -11,23 +11,60 @@ namespace JoshHeaps.Net.Services.Implementations;
 /// </summary>
 public sealed class ComputerMoveOrchestrator(
     IHubContext<ChessHub> chessHub,
-    IChessService chessService) : IComputerMoveOrchestrator
+    IChessService chessService,
+    ILearnedWeightsStore weightsStore) : IComputerMoveOrchestrator
 {
-    public async Task<(MoveDto move, MoveResultDto result)> PlayAsync(GameState state, IChessEngine engine)
+    public async Task<(MoveDto move, MoveResultDto result)> PlayAsync(GameState state)
     {
+        var engine = (state.CurrentPlayer == PieceColor.White ? state.WhiteComputer : state.BlackComputer)
+            ?? throw new InvalidOperationException($"No engine is set for {state.CurrentPlayer} in game {state.GameId}.");
+
         var uci = await engine.GetBestMoveAsync(state.ToFen(), state.RepetitionHistory());
+        var move = uci.ToMoveDto(state, CurrentPlayerId(state));
 
-        var move = uci.ToMoveDto(
+        return await ApplyAndBroadcastAsync(state, move);
-            state,
+    }
-            state.CurrentPlayer == PieceColor.White
-                ? state.WhitePlayerId
-                : state.BlackPlayerId);
 
+    public async Task PlayRandomMoveAsync(GameState state)
+    {
+        var options = chessService.GetAllLegalMoves(state);
+
+        // No legal moves means the game is already over; let the caller's loop detect it.
+        if (options.Count == 0)
+            return;
+
+        var (piece, moves) = options[Random.Shared.Next(options.Count)];
+        var target = moves[Random.Shared.Next(moves.Count)];
+
+        var move = new MoveDto
+        {
+            GameId = state.GameId,
+            PlayerId = CurrentPlayerId(state),
+            PieceId = piece.Id,
+            SourceRow = piece.Position.Row,
+            SourceCol = piece.Position.Col,
+            TargetRow = target.Row,
+            TargetCol = target.Col,
+            PromotionChoice = null   // a pawn cannot reach the last rank within the opening plies
+        };
+
+        await ApplyAndBroadcastAsync(state, move);
+    }
+
+    private async Task<(MoveDto move, MoveResultDto result)> ApplyAndBroadcastAsync(GameState state, MoveDto move)
+    {
         var result = chessService.MakeMove(state, move);
 
+        // Record the played position for training (no-op for non-training games).
+        if (state.Trainer != nint.Zero)
+            weightsStore.Record(state.Trainer, state.ToFen());
+
         await chessHub.Clients.Group(state.GameId.ToString())
             .SendAsync("ReceiveMoveUpdate", state.GameId.ToString(), move, result, state.ToDto());
 
         return (move, result);
     }
+
+    private static Guid CurrentPlayerId(GameState state) =>
+        state.CurrentPlayer == PieceColor.White ? state.WhitePlayerId : state.BlackPlayerId;
 }

JoshHeaps.Net/Services/Implementations/CustomChessEngine.cs

@@ -5,6 +5,16 @@ using System.Runtime.InteropServices;
 
 namespace JoshHeaps.Net.Services.Implementations;
 
+/// <summary>Which evaluation the native engine uses.</summary>
+public enum EngineVariant
+{
+    /// <summary>The hand-crafted evaluation.</summary>
+    Classic,
+
+    /// <summary>Material plus a learned per-square bonus table loaded from a weights file.</summary>
+    Learned
+}
+
 /// <summary>
 /// Middleman wrapper over the native custom chess engine (chess_engine.dll / libchess_engine.so).
 /// Shares <see cref="IChessEngine"/> with <see cref="Stockfish"/> so the two are swappable.
@@ -16,11 +26,17 @@ public sealed partial class CustomChessEngine : IChessEngine
 
     public int Skill { get; }
 
-    public CustomChessEngine(int skill = 20)
+    public CustomChessEngine(int skill = 20, EngineVariant variant = EngineVariant.Classic, string? weightsPath = null)
     {
         Skill = skill;
 
-        var handle = NativeMethods.engine_create($"skill={skill}");
+        // weights= must come last: the native side reads the path as the rest of the
+        // string, which lets it contain ';' and spaces.
+        var options = variant == EngineVariant.Learned
+            ? $"skill={skill};variant=learned;weights={weightsPath}"
+            : $"skill={skill}";
+
+        var handle = NativeMethods.engine_create(options);
 
         if (handle == IntPtr.Zero)
             throw new InvalidOperationException("Native chess engine failed to initialize (engine_create returned null).");
@@ -79,8 +95,9 @@ public sealed partial class CustomChessEngine : IChessEngine
     /// <summary>
     /// P/Invoke surface for chess_engine.(dll|so). The resolver maps the logical name
     /// "chess_engine" to the platform binary in the Resources folder (mirrors Stockfish).
+    /// Internal so <see cref="LearnedWeightsStore"/> can share the single import resolver.
     /// </summary>
-    private static partial class NativeMethods
+    internal static partial class NativeMethods
     {
         private const string LibName = "chess_engine";
 
@@ -115,5 +132,31 @@ public sealed partial class CustomChessEngine : IChessEngine
         [LibraryImport(LibName)]
         [UnmanagedCallConv(CallConvs = [typeof(CallConvCdecl)])]
         internal static partial void engine_destroy(IntPtr engine);
+
+        // ---- Learned-weights / training ABI (see chess_engine.h) ----
+
+        [LibraryImport(LibName, StringMarshalling = StringMarshalling.Utf8)]
+        [UnmanagedCallConv(CallConvs = [typeof(CallConvCdecl)])]
+        internal static partial void learned_load(string path);
+
+        [LibraryImport(LibName)]
+        [UnmanagedCallConv(CallConvs = [typeof(CallConvCdecl)])]
+        internal static unsafe partial int weights_snapshot(int* outBuf, int outLen);
+
+        [LibraryImport(LibName)]
+        [UnmanagedCallConv(CallConvs = [typeof(CallConvCdecl)])]
+        internal static partial IntPtr trainer_create();
+
+        [LibraryImport(LibName, StringMarshalling = StringMarshalling.Utf8)]
+        [UnmanagedCallConv(CallConvs = [typeof(CallConvCdecl)])]
+        internal static partial void trainer_record(IntPtr trainer, string fen);
+
+        [LibraryImport(LibName)]
+        [UnmanagedCallConv(CallConvs = [typeof(CallConvCdecl)])]
+        internal static partial void trainer_apply(IntPtr trainer, int winner, double weight);
+
+        [LibraryImport(LibName)]
+        [UnmanagedCallConv(CallConvs = [typeof(CallConvCdecl)])]
+        internal static partial void trainer_destroy(IntPtr trainer);
     }
 }

JoshHeaps.Net/Services/Implementations/LearnedWeightsStore.cs

@@ -0,0 +1,64 @@
+using JoshHeaps.Net.Models;
+using JoshHeaps.Net.Services.Interfaces;
+
+namespace JoshHeaps.Net.Services.Implementations;
+
+/// <summary>
+/// Managed facade over the native learned-weights model (see <see cref="ILearnedWeightsStore"/>).
+/// On construction it points the native engine at the weights file; everything else delegates
+/// to the shared native ABI in <see cref="CustomChessEngine"/>.
+/// </summary>
+public sealed class LearnedWeightsStore : ILearnedWeightsStore
+{
+    private const int Pieces = 6;       // Pawn..King
+    private const int Squares = 64;
+    private const int Features = 8;     // mobility N/B/R/Q, passed, isolated, doubled, king safety
+
+    public string WeightsFilePath { get; }
+
+    public LearnedWeightsStore(IHostEnvironment env)
+    {
+        WeightsFilePath = Path.Combine(env.ContentRootPath, "chess-data", "learned-weights.txt");
+        Directory.CreateDirectory(Path.GetDirectoryName(WeightsFilePath)!);
+        CustomChessEngine.NativeMethods.learned_load(WeightsFilePath);
+    }
+
+    public nint CreateTrainer() => CustomChessEngine.NativeMethods.trainer_create();
+
+    public void Record(nint trainer, string fen) =>
+        CustomChessEngine.NativeMethods.trainer_record(trainer, fen);
+
+    public void ApplyResult(nint trainer, PieceColor winner, double weight) =>
+        CustomChessEngine.NativeMethods.trainer_apply(trainer, winner == PieceColor.White ? 0 : 1, weight);
+
+    public void DestroyTrainer(nint trainer) =>
+        CustomChessEngine.NativeMethods.trainer_destroy(trainer);
+
+    public LearnedWeightsSnapshot Snapshot()
+    {
+        const int total = Pieces * Squares * 2 + Features;
+        var buffer = new int[total];
+
+        unsafe
+        {
+            fixed (int* p = buffer)
+                CustomChessEngine.NativeMethods.weights_snapshot(p, total);
+        }
+
+        var mg = new int[Pieces][];
+        var eg = new int[Pieces][];
+
+        for (int piece = 0; piece < Pieces; piece++)
+        {
+            mg[piece] = new int[Squares];
+            eg[piece] = new int[Squares];
+            Array.Copy(buffer, piece * Squares, mg[piece], 0, Squares);
+            Array.Copy(buffer, Pieces * Squares + piece * Squares, eg[piece], 0, Squares);
+        }
+
+        var features = new int[Features];
+        Array.Copy(buffer, Pieces * Squares * 2, features, 0, Features);
+
+        return new LearnedWeightsSnapshot(mg, eg, features);
+    }
+}

JoshHeaps.Net/Services/Interfaces/IChessEngineFactory.cs

@@ -1,3 +1,5 @@
+using JoshHeaps.Net.Services.Implementations;
+
 namespace JoshHeaps.Net.Services.Interfaces;
 
 /// <summary>
@@ -6,9 +8,20 @@ namespace JoshHeaps.Net.Services.Interfaces;
 public interface IChessEngineFactory
 {
     /// <summary>
-    /// Creates a new engine instance for a single game. The caller owns and disposes it.
+    /// Creates a new engine instance for a single game using the configured default engine.
+    /// The caller owns and disposes it.
     /// </summary>
     /// <param name="skill">The desired playing strength / search depth.</param>
     /// <returns>A new, owned <see cref="IChessEngine"/>.</returns>
     IChessEngine Create(int skill);
+
+    /// <summary>
+    /// Creates a new engine instance for a single game using an explicitly chosen engine
+    /// (e.g. for picking a different engine per side in a CPU-vs-CPU game). The caller owns
+    /// and disposes it.
+    /// </summary>
+    /// <param name="skill">The desired playing strength / search depth.</param>
+    /// <param name="kind">The engine implementation to create.</param>
+    /// <returns>A new, owned <see cref="IChessEngine"/>.</returns>
+    IChessEngine Create(int skill, ChessEngineKind kind);
 }

JoshHeaps.Net/Services/Interfaces/IComputerMoveOrchestrator.cs

@@ -3,16 +3,24 @@ using JoshHeaps.Net.Models;
 namespace JoshHeaps.Net.Services.Interfaces;
 
 /// <summary>
-/// Drives a computer move: asks the engine for a move, applies it through the rules
+/// Drives a computer move: asks the side-to-move's engine for a move, applies it through
-/// service, and broadcasts the result to the game's clients.
+/// the rules service, and broadcasts the result to the game's clients.
 /// </summary>
 public interface IComputerMoveOrchestrator
 {
     /// <summary>
-    /// Has the engine pick a move for the current position, applies it, and broadcasts it.
+    /// Has the side-to-move's engine pick a move for the current position, applies it, and
+    /// broadcasts it. The engine is taken from the game's per-side computer assignments.
     /// </summary>
     /// <param name="state">The game to play a move in.</param>
-    /// <param name="engine">The engine that selects the move.</param>
     /// <returns>The applied move and its result.</returns>
-    Task<(MoveDto move, MoveResultDto result)> PlayAsync(GameState state, IChessEngine engine);
+    Task<(MoveDto move, MoveResultDto result)> PlayAsync(GameState state);
+
+    /// <summary>
+    /// Plays a uniformly-random legal move for the side to move (no engine), applying and
+    /// broadcasting it. Used to randomize the opening of training games so self-play and
+    /// engine-vs-engine games don't replay the same line every time.
+    /// </summary>
+    /// <param name="state">The game to play a random move in.</param>
+    Task PlayRandomMoveAsync(GameState state);
 }

JoshHeaps.Net/Services/Interfaces/ILearnedWeightsStore.cs

@@ -0,0 +1,33 @@
+using JoshHeaps.Net.Models;
+
+namespace JoshHeaps.Net.Services.Interfaces;
+
+/// <summary>
+/// Thin managed facade over the native learned-weights model. The weights, all feature
+/// computation, per-game accumulation, the update rule, and persistence live in the native
+/// engine; this just points it at the weights file, hands out per-game trainers, and reads
+/// the table back for visualization.
+/// </summary>
+public interface ILearnedWeightsStore
+{
+    /// <summary>Absolute path to the weights file the native engine loads and saves.</summary>
+    string WeightsFilePath { get; }
+
+    /// <summary>A copy of the current weights (midgame/endgame tables + feature weights).</summary>
+    LearnedWeightsSnapshot Snapshot();
+
+    /// <summary>Creates a per-game training accumulator. The caller owns it (see <see cref="DestroyTrainer"/>).</summary>
+    nint CreateTrainer();
+
+    /// <summary>Records one played position (post-move FEN) into a trainer.</summary>
+    void Record(nint trainer, string fen);
+
+    /// <summary>
+    /// Applies a finished game's outcome to the global weights (and saves): rewards the
+    /// winner's squares/features, punishes the loser's, scaled by <paramref name="weight"/>.
+    /// </summary>
+    void ApplyResult(nint trainer, PieceColor winner, double weight);
+
+    /// <summary>Frees a trainer. Safe to call with <see cref="nint.Zero"/>.</summary>
+    void DestroyTrainer(nint trainer);
+}

JoshHeaps.Net/wwwroot/css/chess/spectate.css

@@ -21,11 +21,27 @@ html, body {
     display: flex;
     align-items: center;
     justify-content: center;
-    gap: 0.5rem;
+    flex-wrap: wrap;
+    gap: 0.75rem;
     margin: 1rem 0;
 }
 
-#cpuDifficulty {
+.enginePicker {
+    display: flex;
+    align-items: center;
+    gap: 0.4rem;
+    border: 1px solid #444;
+    border-radius: 8px;
+    padding: 0.3rem 0.6rem 0.5rem;
+}
+
+.enginePicker legend {
+    color: #9a9a9a;
+    font-size: 0.8rem;
+    padding: 0 0.3rem;
+}
+
+#watchControls select {
     background-color: #1e1e1e;
     color: #d6d6d6;
     border: 1px solid #444;

JoshHeaps.Net/wwwroot/css/chess/weights.css

@@ -0,0 +1,187 @@
+body {
+    margin: 0;
+    background-color: #141414;
+    color: #d6d6d6;
+    font-family: "Segoe UI", system-ui, sans-serif;
+}
+
+#weightsHeader {
+    text-align: center;
+    padding: 1.5rem 1rem 0.5rem;
+}
+
+#weightsHeader h1 {
+    margin: 0 0 0.25rem;
+    font-size: 1.6rem;
+}
+
+#weightsStatus {
+    color: #9a9a9a;
+    margin: 0.25rem 0 1rem;
+}
+
+#weightsControls {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 1.25rem;
+    margin-bottom: 0.5rem;
+}
+
+.heatLegend {
+    display: flex;
+    align-items: center;
+    gap: 0.5rem;
+    font-size: 0.8rem;
+    color: #9a9a9a;
+}
+
+.legendBar {
+    width: 120px;
+    height: 10px;
+    border-radius: 5px;
+    background: linear-gradient(to right, rgba(232, 74, 74, 1), #2a2a2a, rgba(74, 134, 232, 1));
+}
+
+#refreshWeights {
+    background-color: #8cd5ed;
+    color: #262626;
+    border: 0;
+    border-radius: 30px;
+    padding: 0.5rem 1.1rem;
+    cursor: pointer;
+}
+
+#refreshWeights:hover {
+    background-color: #a5e0f2;
+}
+
+#backToWatch {
+    display: inline-block;
+    margin-top: 0.5rem;
+    color: #8cd5ed;
+    text-decoration: none;
+}
+
+.weightsSection {
+    margin: 0 auto;
+    max-width: 1200px;
+    padding: 0.5rem 1rem;
+}
+
+.weightsSection h2 {
+    text-align: center;
+    font-size: 1.2rem;
+    margin: 1rem 0 0.25rem;
+}
+
+.sectionHint {
+    text-align: center;
+    color: #9a9a9a;
+    font-size: 0.8rem;
+    margin: 0 0 0.75rem;
+}
+
+.weightsGrid {
+    display: flex;
+    flex-wrap: wrap;
+    justify-content: center;
+    gap: 1.25rem;
+    padding: 0.5rem 0;
+}
+
+.featurePanel {
+    max-width: 520px;
+    margin: 0 auto;
+    display: flex;
+    flex-direction: column;
+    gap: 0.4rem;
+}
+
+.featureRow {
+    display: flex;
+    align-items: center;
+    gap: 0.6rem;
+}
+
+.featureLabel {
+    flex: 0 0 6.5rem;
+    font-size: 0.8rem;
+    color: #c8c8c8;
+    text-align: right;
+}
+
+.featureTrack {
+    flex: 1 1 auto;
+    height: 14px;
+    background-color: #232323;
+    border-radius: 7px;
+    overflow: hidden;
+}
+
+.featureBar {
+    height: 100%;
+    border-radius: 7px;
+    min-width: 2px;
+}
+
+.featureValue {
+    flex: 0 0 3rem;
+    font-size: 0.8rem;
+    color: #e8e8e8;
+    text-align: left;
+}
+
+.weightBoard {
+    background-color: #1c1c1c;
+    border: 1px solid #2e2e2e;
+    border-radius: 10px;
+    padding: 0.75rem;
+}
+
+.weightBoardHeader {
+    display: flex;
+    align-items: center;
+    gap: 0.5rem;
+    margin-bottom: 0.5rem;
+    font-weight: 600;
+}
+
+.weightBoardIcon {
+    width: 22px;
+    height: 22px;
+}
+
+.weightRange {
+    margin-left: auto;
+    font-weight: 400;
+    font-size: 0.75rem;
+    color: #888;
+}
+
+.miniHeat {
+    display: grid;
+    grid-template-columns: 1.1rem repeat(8, 34px);
+}
+
+.heatSquare {
+    width: 34px;
+    height: 34px;
+    box-sizing: border-box;
+    border: 1px solid #2a2a2a;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    font-size: 0.62rem;
+    color: #f0f0f0;
+    text-shadow: 0 1px 2px rgba(0, 0, 0, 0.8);
+}
+
+.heatLabel {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    padding: 2px 0;
+    font-size: 0.6rem;
+    color: #777;
+}

JoshHeaps.Net/wwwroot/js/ChessScripts/Spectate.js

@@ -27,10 +27,15 @@ const Spectate = {
     },
 
     async startCpuGame() {
-        const difficulty = document.getElementById("cpuDifficulty").value;
+        const params = new URLSearchParams({
+            whiteEngine: document.getElementById("whiteEngine").value,
+            whiteSkill: document.getElementById("whiteSkill").value,
+            blackEngine: document.getElementById("blackEngine").value,
+            blackSkill: document.getElementById("blackSkill").value
+        });
 
         try {
-            await fetch(`/api/chess/watch/cpu/${difficulty}`);
+            await fetch(`/api/chess/watch/cpu?${params}`);
             await this.refreshGames();
         } catch (err) {
             console.error("❌ Could not start CPU vs CPU game.", err);
@@ -68,7 +73,9 @@ const Spectate = {
     async addGame(game) {
         this.games.set(game.gameId, {
             isVsComputer: game.isVsComputer,
-            isComputerVsComputer: game.isComputerVsComputer
+            isComputerVsComputer: game.isComputerVsComputer,
+            whiteEngine: game.whiteEngine,
+            blackEngine: game.blackEngine
         });
 
         const card = document.createElement("div");
@@ -183,11 +190,21 @@ const Spectate = {
     },
 
     gameLabel(stored) {
-        if (stored.isComputerVsComputer) return "CPU vs CPU";
+        if (stored.isComputerVsComputer)
+            return `${this.engineName(stored.whiteEngine)} (W) vs ${this.engineName(stored.blackEngine)} (B)`;
         if (stored.isVsComputer) return "Vs CPU";
         return "Player vs Player";
     },
 
+    engineName(kind) {
+        switch (kind) {
+            case "CustomLearned": return "Learned";
+            case "Custom": return "Custom";
+            case "Stockfish": return "Stockfish";
+            default: return kind || "CPU";
+        }
+    },
+
     headerText(stored, currentPlayer, moveCount, isCheck) {
         if (stored.result)
             return `${this.gameLabel(stored)} · move ${moveCount} · final`;

JoshHeaps.Net/wwwroot/js/ChessScripts/Weights.js

@@ -0,0 +1,158 @@
+const Weights = {
+    async init() {
+        await this.refresh();
+    },
+
+    async refresh() {
+        let data;
+
+        try {
+            const response = await fetch("/api/chess/weights");
+            data = await response.json();
+        } catch (err) {
+            console.error("❌ Could not load weights.", err);
+            document.getElementById("weightsStatus").textContent = "Could not load weights.";
+            return;
+        }
+
+        this.renderBoardSet(data.mg, "weightsGridMg");
+        this.renderBoardSet(data.eg, "weightsGridEg");
+        this.renderFeatures(data.features);
+
+        const trained = [...data.mg, ...data.eg].some(b => b.squares.some(v => v !== 0))
+            || data.features.some(f => f.value !== 0);
+
+        document.getElementById("weightsStatus").textContent = trained
+            ? "Where the learned engine thinks each piece belongs. Blue = preferred, red = avoided. Midgame vs endgame tables are blended by how much material is left."
+            : "No training yet — everything is neutral. Run some learned games on the Watch page.";
+    },
+
+    // pieces: [{ name, squares[64] }]. Prepends an "Overall" board summing the set.
+    renderBoardSet(pieces, containerId) {
+        const overall = new Array(64).fill(0);
+        for (const piece of pieces)
+            for (let sq = 0; sq < 64; sq++)
+                overall[sq] += piece.squares[sq];
+
+        const boards = [{ name: "Overall", squares: overall }, ...pieces];
+        const grid = document.getElementById(containerId);
+        grid.innerHTML = "";
+        boards.forEach(board => grid.appendChild(this.buildBoard(board)));
+    },
+
+    buildBoard(board) {
+        const wrapper = document.createElement("div");
+        wrapper.className = "weightBoard";
+
+        const maxAbs = board.squares.reduce((m, v) => Math.max(m, Math.abs(v)), 0);
+
+        const header = document.createElement("div");
+        header.className = "weightBoardHeader";
+        if (board.name !== "Overall") {
+            const icon = document.createElement("img");
+            icon.src = `/images/Chess Images/White${board.name}.svg`;
+            icon.alt = board.name;
+            icon.className = "weightBoardIcon";
+            header.appendChild(icon);
+        }
+        const title = document.createElement("span");
+        title.textContent = board.name;
+        header.appendChild(title);
+        const range = document.createElement("span");
+        range.className = "weightRange";
+        range.textContent = maxAbs === 0 ? "neutral" : `±${maxAbs}`;
+        header.appendChild(range);
+        wrapper.appendChild(header);
+
+        const heat = document.createElement("div");
+        heat.className = "miniHeat";
+
+        for (let row = 0; row < 8; row++) {
+            const rankLabel = document.createElement("div");
+            rankLabel.className = "heatLabel";
+            rankLabel.textContent = 8 - row;          // rank 8 at top, 1 at bottom
+            heat.appendChild(rankLabel);
+
+            for (let col = 0; col < 8; col++) {
+                const rank = 7 - row;                 // rank index, 0 = rank 1
+                const sq = rank * 8 + col;            // white-relative square (A1 = 0)
+                heat.appendChild(this.buildSquare(board.squares[sq], sq, maxAbs));
+            }
+        }
+
+        heat.appendChild(this.cornerSpacer());
+        for (let col = 0; col < 8; col++) {
+            const fileLabel = document.createElement("div");
+            fileLabel.className = "heatLabel";
+            fileLabel.textContent = String.fromCharCode(97 + col);
+            heat.appendChild(fileLabel);
+        }
+
+        wrapper.appendChild(heat);
+        return wrapper;
+    },
+
+    buildSquare(value, sq, maxAbs) {
+        const cell = document.createElement("div");
+        cell.className = "heatSquare";
+
+        if (value !== 0 && maxAbs > 0) {
+            const ratio = Math.abs(value) / maxAbs;
+            const alpha = (0.12 + 0.88 * ratio).toFixed(3);
+            cell.style.backgroundColor = value > 0
+                ? `rgba(74, 134, 232, ${alpha})`      // high -> blue
+                : `rgba(232, 74, 74, ${alpha})`;      // low  -> red
+            cell.textContent = value;
+        }
+
+        const file = String.fromCharCode(97 + (sq & 7));
+        const rank = (sq >> 3) + 1;
+        cell.title = `${file}${rank}: ${value}`;
+        return cell;
+    },
+
+    cornerSpacer() {
+        const spacer = document.createElement("div");
+        spacer.className = "heatLabel";
+        return spacer;
+    },
+
+    // features: [{ name, value }]
+    renderFeatures(features) {
+        const panel = document.getElementById("featureWeights");
+        panel.innerHTML = "";
+
+        const maxAbs = features.reduce((m, f) => Math.max(m, Math.abs(f.value)), 0);
+
+        features.forEach(feature => {
+            const row = document.createElement("div");
+            row.className = "featureRow";
+
+            const label = document.createElement("span");
+            label.className = "featureLabel";
+            label.textContent = feature.name;
+
+            const track = document.createElement("div");
+            track.className = "featureTrack";
+            const bar = document.createElement("div");
+            bar.className = "featureBar";
+            const ratio = maxAbs === 0 ? 0 : Math.abs(feature.value) / maxAbs;
+            bar.style.width = `${(ratio * 100).toFixed(1)}%`;
+            bar.style.backgroundColor = feature.value >= 0
+                ? "rgba(74, 134, 232, 0.85)"
+                : "rgba(232, 74, 74, 0.85)";
+            track.appendChild(bar);
+
+            const value = document.createElement("span");
+            value.className = "featureValue";
+            value.textContent = feature.value;
+
+            row.append(label, track, value);
+            panel.appendChild(row);
+        });
+    }
+};
+
+window.addEventListener("load", () => Weights.init());
+
+console.log("Weights.js loaded");

native/chess_engine/include/chess_engine.h

@@ -32,6 +32,9 @@ extern "C" {                          /* prevent C++ name mangling */
  * Internally it points to your engine state object. */
 typedef struct ChessEngine* EngineHandle;
 
+/* Opaque per-game training accumulator (see the learned-weights ABI at the bottom). */
+typedef struct Trainer* TrainerHandle;
+
 /* Return codes. 0 == success; negative == error. Keep these values stable. */
 enum {
     CHESS_OK              =  0,
@@ -79,6 +82,32 @@ CHESS_API int CHESS_CALL engine_version(char* out_buf, int out_len);
 /* Destroy an instance created by engine_create. Safe to call with NULL. */
 CHESS_API void CHESS_CALL engine_destroy(EngineHandle engine);
 
+/* ---- Learned-weights / training ABI ----
+ * The learned engine's weights live process-globally here. The host orchestrates games but
+ * owns no chess logic: it points the engine at the weights file, records each played
+ * position, and applies the game's result. */
+
+/* Load the global learned weights from `path` and remember it for later saves. Idempotent;
+ * a missing/short file leaves the weights neutral. Call once before learned play/training. */
+CHESS_API void CHESS_CALL learned_load(const char* path);
+
+/* Copy the global weights out for visualization: 6*64 midgame + 6*64 endgame (PAWN..KING,
+ * squares 0..63) + feature weights. Returns the count written, or CHESS_ERR_BUFFER if
+ * out_len is too small (needs >= 776). */
+CHESS_API int CHESS_CALL weights_snapshot(int* out, int out_len);
+
+/* Create / destroy a per-game training accumulator. Safe to destroy NULL. */
+CHESS_API TrainerHandle CHESS_CALL trainer_create(void);
+CHESS_API void          CHESS_CALL trainer_destroy(TrainerHandle trainer);
+
+/* Record one played position (post-move FEN) into the accumulator. */
+CHESS_API void CHESS_CALL trainer_record(TrainerHandle trainer, const char* fen);
+
+/* Apply a finished game's outcome to the global weights and save: rewards the winner's
+ * occupied squares / features and punishes the loser's, scaled by `weight` (e.g. 0.5 for a
+ * material-imbalance draw). winner: 0 = white, 1 = black. */
+CHESS_API void CHESS_CALL trainer_apply(TrainerHandle trainer, int winner, double weight);
+
 #ifdef __cplusplus
 }
 #endif

native/chess_engine/src/chess_engine.cpp

@@ -20,10 +20,12 @@
 
 #include <algorithm>
 #include <atomic>
+#include <cmath>
 #include <cstdint>
 #include <cstdio>
 #include <cstdlib>
 #include <cstring>
+#include <fstream>
 #include <mutex>
 #include <new>
 #include <string>
@@ -76,10 +78,63 @@ static chess::Move tt_move (uint64_t d) { return chess::Move(static_cast<uint16_
 static int         tt_depth(uint64_t d) { return static_cast<int>(static_cast<uint8_t>(d >> 48)); }
 static Bound       tt_bound(uint64_t d) { return static_cast<Bound>(static_cast<uint8_t>(d >> 56)); }
 
-/* Internal engine state. One ChessEngine = one game. The table is NOT here: it is the
+/* Eval variant for an engine handle. CLASSIC = the hand-crafted evaluate(); LEARNED =
- * shared g_tt above. */
+ * material + learned phase-split piece-square tables + learned feature weights. */
+enum EvalVariant : int { EVAL_CLASSIC = 0, EVAL_LEARNED = 1 };
+
+/* The learned feature knobs (beyond the piece-square tables). Each has one weight learned
+ * from game outcomes; its activation is computed by compute_features(). Mobility is per
+ * piece type. Order is fixed — it is the on-disk and snapshot layout after the two tables. */
+enum Feature : int {
+    FEAT_MOB_N, FEAT_MOB_B, FEAT_MOB_R, FEAT_MOB_Q,   /* legal-move counts, per piece type */
+    FEAT_PASSED,                                       /* passed pawns, endgame-weighted     */
+    FEAT_ISOLATED,                                     /* isolated pawns                     */
+    FEAT_DOUBLED,                                       /* doubled pawns                      */
+    FEAT_KING,                                          /* king pawn-shelter, midgame-weighted */
+    FEATURE_NB
+};
+
+/* Per-handle eval configuration, snapshotted from the global learned weights at
+ * engine_create so the search reads a stable copy. The tables are white-relative: a black
+ * piece indexes the rank-mirrored square (sq ^ 56). `mg`/`eg` are blended by game phase.
+ * Indexed by chess::PieceType (PAWN..KING). Only consulted when variant == EVAL_LEARNED. */
+struct EvalParams {
+    int variant = EVAL_CLASSIC;
+    int mg[chess::PIECE_TYPE_NB][64] = {};
+    int eg[chess::PIECE_TYPE_NB][64] = {};
+    int featW[FEATURE_NB] = {};
+};
+
+/* Internal engine state. One ChessEngine = one game. The transposition table is NOT
+ * here: it is the shared g_tt above. */
 struct ChessEngine {
-    int skill = 20;          /* 1..20 from the UI; controls search depth */
+    int        skill = 20;   /* 1..20 from the UI; controls search depth */
+    EvalParams eval;         /* which evaluation the search uses, plus any learned weights */
+};
+
+/* The process-global learned weights: the single source of truth, loaded from disk once and
+ * updated in place by training. Engine handles snapshot it at creation; the visualization
+ * snapshots it on demand. Guarded by g_weightsMutex for updates/saves (eval reads its own
+ * per-handle copy, so it never touches this concurrently). */
+struct LearnedWeights {
+    int mg[chess::PIECE_TYPE_NB][64] = {};
+    int eg[chess::PIECE_TYPE_NB][64] = {};
+    int featW[FEATURE_NB] = {};
+};
+
+static LearnedWeights g_weights;
+static std::mutex     g_weightsMutex;
+static std::string    g_weightsPath;
+
+/* Per-game training accumulator (one per learned CPU-vs-CPU game). Records, per ply, where
+ * each side's pieces sat (split into midgame/endgame by phase) and each side's feature
+ * activations; trainer_apply turns the totals into weight nudges. Squares are white-relative
+ * (black indexes sq ^ 56), so a side's tally lines up with the shared white-relative table. */
+struct Trainer {
+    double mgOcc[chess::COLOR_NB][chess::PIECE_TYPE_NB][64] = {};
+    double egOcc[chess::COLOR_NB][chess::PIECE_TYPE_NB][64] = {};
+    double featAcc[chess::COLOR_NB][FEATURE_NB] = {};
+    int    plies = 0;
 };
 
 static int copy_out(const char* src, char* out_buf, int out_len) {
@@ -108,6 +163,48 @@ static int parse_skill(const char* options, int fallback) {
     return v < 1 ? 1 : v > 20 ? 20 : v;
 }
 
+/* "variant=learned" in the options selects the learned eval; anything else is classic. */
+static int parse_variant(const char* options) {
+    if (!options) return EVAL_CLASSIC;
+    const char* p = std::strstr(options, "variant=");
+    if (!p) return EVAL_CLASSIC;
+    return std::strncmp(p + 8, "learned", 7) == 0 ? EVAL_LEARNED : EVAL_CLASSIC;
+}
+
+/* On-disk format: 6*64 mg ints (PAWN..KING, squares 0..63), then 6*64 eg ints, then
+ * FEATURE_NB feature ints, whitespace-separated. A missing file or short read leaves the
+ * rest neutral (0), so an absent weights file just means "train from a blank slate".
+ * Caller holds g_weightsMutex. */
+static void load_global_weights(const char* path) {
+    g_weights = LearnedWeights{};                    /* reset to neutral before loading */
+
+    if (!path || !*path) return;
+    std::ifstream f(path);
+    if (!f) return;
+
+    for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
+        for (int sq = 0; sq < 64; ++sq)
+            if (!(f >> g_weights.mg[pt][sq])) return;
+    for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
+        for (int sq = 0; sq < 64; ++sq)
+            if (!(f >> g_weights.eg[pt][sq])) return;
+    for (int i = 0; i < FEATURE_NB; ++i)
+        if (!(f >> g_weights.featW[i])) return;
+}
+
+/* Persist g_weights to g_weightsPath in the format load_global_weights reads. Caller holds the lock. */
+static void save_global_weights() {
+    if (g_weightsPath.empty()) return;
+    std::ofstream f(g_weightsPath);
+    if (!f) return;
+
+    for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
+        for (int sq = 0; sq < 64; ++sq) f << g_weights.mg[pt][sq] << (sq == 63 ? '\n' : ' ');
+    for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
+        for (int sq = 0; sq < 64; ++sq) f << g_weights.eg[pt][sq] << (sq == 63 ? '\n' : ' ');
+    for (int i = 0; i < FEATURE_NB; ++i) f << g_weights.featW[i] << (i == FEATURE_NB - 1 ? '\n' : ' ');
+}
+
 /* Maps the 1..20 difficulty to a search depth. Kept modest: the search has no
  * quiescence yet, so deep fixed-depth runs get expensive quickly. */
 static int depth_for_skill(int skill) {
@@ -279,10 +376,136 @@ static int evaluate(const chess::Position& pos) {
     return score;
 }
 
+/* ---- Learned (phase-split tables + feature knobs) evaluation ---------------------------
+ * The model is a linear combination of features whose weights are learned from outcomes:
+ *   eval = Σ pieces [ material + blend(mg, eg, phase) ]  +  Σ features featW[i]·activation[i]
+ * compute_features() is the single source of feature activations, used by BOTH the eval here
+ * and the trainer, so the two can never disagree. Constants below are the only tunables. */
+
+/* Per-game-outcome learning rates and clamps. Squares accumulate occupancy (plies on a
+ * square, summed); features accumulate normalized per-ply activation (averaged, divided by a
+ * nominal scale so high-magnitude mobility doesn't dwarf the small pawn-structure terms). */
+static constexpr double SQUARE_LR  = 0.5;
+static constexpr int    SQ_CLAMP   = 250;
+static constexpr double FEAT_LR    = 2.0;
+static constexpr int    FEAT_CLAMP = 500;
+static constexpr double FEAT_SCALE[FEATURE_NB] = { 4, 6, 8, 14, 2, 1, 1, 2 };
+
+/* Game phase in [0,1] from remaining non-pawn material (PeSTO weights N=B=1, R=2, Q=4; max
+ * 24 for both full sides): 0 = opening, 1 = bare kings. Drives the mg/eg table blend and
+ * the phase weighting of the passed-pawn (×phase) and king-safety (×(1−phase)) features. */
+static double game_phase(const chess::Position& pos) {
+    int npm = chess::popcount(pos.pieces(chess::KNIGHT)) * 1
+            + chess::popcount(pos.pieces(chess::BISHOP)) * 1
+            + chess::popcount(pos.pieces(chess::ROOK))   * 2
+            + chess::popcount(pos.pieces(chess::QUEEN))  * 4;
+    constexpr int MAX = 24;
+    if (npm >= MAX) return 0.0;
+    return double(MAX - npm) / MAX;
+}
+
+/* Blend a midgame and endgame value by phase, rounding per-piece (so training credits a
+ * square the same way the eval reads it). */
+static int blend(int mg, int eg, double phase) {
+    return int(std::lround((1.0 - phase) * mg + phase * eg));
+}
+
+/* Fills `out[FEATURE_NB]` with one color's raw feature activations for a position. The piece-
+ * square tables handle "where pieces belong"; these capture context a static table can't:
+ * legal mobility (per piece type, so pins reduce it), passed pawns (endgame-weighted), pawn
+ * structure, and king shelter (midgame-weighted). Ported nowhere — this is the only copy. */
+static void compute_features(chess::Position& pos, chess::Color c, double phase, double out[FEATURE_NB]) {
+    for (int i = 0; i < FEATURE_NB; ++i) out[i] = 0.0;
+
+    /* Mobility: legal moves for color c, bucketed by the moving piece's type. */
+    chess::MoveList moves;
+    pos.generate_legal_for(c, moves);
+    for (int i = 0; i < moves.size(); ++i) {
+        switch (chess::type_of(pos.piece_on(moves.moves[i].from()))) {
+            case chess::KNIGHT: out[FEAT_MOB_N] += 1; break;
+            case chess::BISHOP: out[FEAT_MOB_B] += 1; break;
+            case chess::ROOK:   out[FEAT_MOB_R] += 1; break;
+            case chess::QUEEN:  out[FEAT_MOB_Q] += 1; break;
+            default: break;
+        }
+    }
+
+    /* Pawn structure. */
+    chess::Bitboard pawns = pos.pieces(c, chess::PAWN);
+    chess::Bitboard bb = pawns;
+    while (bb) {
+        chess::Square s = chess::pop_lsb(bb);
+
+        if (!(front_span(c, s) & pos.pieces(~c, chess::PAWN))) {          /* passed */
+            chess::Rank r = chess::rank_of(s);
+            int toPromotion = (c == chess::WHITE) ? (chess::RANK_8 - r) : (r - chess::RANK_1);
+            out[FEAT_PASSED] += (6 - toPromotion) * phase;               /* 0..5 ranks advanced, late-game */
+        }
+        if (front_span_file_only(c, s) & pawns)                          /* doubled (friendly pawn ahead) */
+            out[FEAT_DOUBLED] += 1;
+
+        chess::File f = chess::file_of(s);
+        chess::Bitboard adjacent = 0;
+        if (f > chess::FILE_A) adjacent |= chess::file_bb(chess::File(f - 1));
+        if (f < chess::FILE_H) adjacent |= chess::file_bb(chess::File(f + 1));
+        if (!(adjacent & pawns))                                          /* isolated */
+            out[FEAT_ISOLATED] += 1;
+    }
+
+    /* King safety: friendly pawns sheltering the king (its file + adjacent files, the two
+     * ranks in front), worth more in the midgame. */
+    chess::Square k = pos.king_square(c);
+    chess::File kf = chess::file_of(k);
+    chess::Rank kr = chess::rank_of(k);
+    chess::Bitboard kingFiles = chess::file_bb(kf);
+    if (kf > chess::FILE_A) kingFiles |= chess::file_bb(chess::File(kf - 1));
+    if (kf < chess::FILE_H) kingFiles |= chess::file_bb(chess::File(kf + 1));
+    chess::Bitboard shelterRanks = 0;
+    for (int d = 1; d <= 2; ++d) {
+        int rr = (c == chess::WHITE) ? (kr + d) : (kr - d);
+        if (rr >= 0 && rr <= 7) shelterRanks |= (0xFFULL << (8 * rr));
+    }
+    out[FEAT_KING] += chess::popcount(kingFiles & shelterRanks & pawns) * (1.0 - phase);
+}
+
+/* Learned eval (white-positive/absolute, like evaluate()): material + phase-blended piece-
+ * square tables + learned feature weights. Black pieces index the rank-mirrored square
+ * (s ^ 56) so both colors share one white-relative table. Non-const because mobility
+ * generates legal moves (which the position's move generator does via do/undo). */
+static int evaluateLearned(chess::Position& pos, const EvalParams& ep) {
+    double phase = game_phase(pos);
+    int score = 0;
+
+    chess::Bitboard white = pos.pieces(chess::WHITE);
+    while (white) {
+        chess::Square s = chess::pop_lsb(white);
+        chess::PieceType pt = chess::type_of(pos.piece_on(s));
+        score += piece_value(pt) + blend(ep.mg[pt][s], ep.eg[pt][s], phase);
+    }
+
+    chess::Bitboard black = pos.pieces(chess::BLACK);
+    while (black) {
+        chess::Square s = chess::pop_lsb(black);
+        chess::PieceType pt = chess::type_of(pos.piece_on(s));
+        score -= piece_value(pt) + blend(ep.mg[pt][s ^ 56], ep.eg[pt][s ^ 56], phase);
+    }
+
+    double wFeat[FEATURE_NB], bFeat[FEATURE_NB];
+    compute_features(pos, chess::WHITE, phase, wFeat);
+    compute_features(pos, chess::BLACK, phase, bFeat);
+
+    double feature = 0.0;
+    for (int i = 0; i < FEATURE_NB; ++i)
+        feature += ep.featW[i] * (wFeat[i] - bFeat[i]) / FEAT_SCALE[i];
+    score += int(std::lround(feature));
+
+    return score;
+}
+
 /* evaluate() is white-positive (absolute). Negamax needs it relative to the side to
  * move, so flip the sign when black is to move. */
-static int evaluate_stm(const chess::Position& pos, bool whiteToMove) {
+static int evaluate_stm(chess::Position& pos, bool whiteToMove, const EvalParams& ep) {
-    int s = evaluate(pos);
+    int s = (ep.variant == EVAL_LEARNED) ? evaluateLearned(pos, ep) : evaluate(pos);
     return whiteToMove ? s : -s;
 }
 
@@ -358,6 +581,15 @@ CHESS_API EngineHandle CHESS_CALL engine_create(const char* options) {
     auto* e = new (std::nothrow) ChessEngine();
     if (!e) return nullptr;
     e->skill = parse_skill(options, e->skill);
+    e->eval.variant = parse_variant(options);
+    if (e->eval.variant == EVAL_LEARNED) {
+        /* Snapshot the current global weights so the search reads a stable copy (training
+         * updates the global between games; the weights path is owned by learned_load). */
+        std::lock_guard<std::mutex> lock(g_weightsMutex);
+        std::memcpy(e->eval.mg,    g_weights.mg,    sizeof e->eval.mg);
+        std::memcpy(e->eval.eg,    g_weights.eg,    sizeof e->eval.eg);
+        std::memcpy(e->eval.featW, g_weights.featW, sizeof e->eval.featW);
+    }
     return e;
 }
 
@@ -376,8 +608,9 @@ CHESS_API int CHESS_CALL engine_set_option(EngineHandle engine,
 static constexpr int MAX_PLY = 128;            /* ply never exceeds maxDepth (<= 20) */
 
 struct SearchContext {
-    uint64_t    nodes = 0;
+    uint64_t          nodes = 0;
-    chess::Move killers[MAX_PLY][2] = {};      /* [ply][slot]; MOVE_NONE until filled */
+    const EvalParams* eval  = nullptr;         /* eval config for this search; set by engine_best_move */
+    chess::Move       killers[MAX_PLY][2] = {};/* [ply][slot]; MOVE_NONE until filled */
 };
 
 /* Negamax alpha-beta over the shared transposition table. `maxDepth` is the searching
@@ -394,7 +627,7 @@ static int negamax(chess::Position& pos, int maxDepth, int depth, int ply,
         return 0;
 
     if (depth <= 0)
-        return evaluate_stm(pos, whiteToMove);
+        return evaluate_stm(pos, whiteToMove, *ctx.eval);
 
     const uint64_t key  = pos.key();
     TTEntry&       slot = g_tt.entries[key & g_tt.mask];
@@ -509,6 +742,7 @@ CHESS_API int CHESS_CALL engine_best_move(EngineHandle engine,
         return CHESS_ERR_NO_MOVE;
 
     SearchContext ctx;
+    ctx.eval = &engine->eval;
     int maxDepth = depth_for_skill(engine->skill);
     chess::Move bestMove = moves.moves[0];            /* guaranteed-legal fallback */
 
@@ -554,4 +788,100 @@ CHESS_API void CHESS_CALL engine_destroy(EngineHandle engine) {
     delete engine;                                        /* delete nullptr is safe */
 }
 
+/* ---- Learned-weights / training C ABI --------------------------------------------------
+ * The managed side orchestrates games but owns no chess logic: it tells the engine where
+ * to load/save the global weights, records each played position, and applies the result. */
+
+CHESS_API void CHESS_CALL learned_load(const char* path) {
+    std::lock_guard<std::mutex> lock(g_weightsMutex);
+    g_weightsPath = path ? path : "";
+    load_global_weights(path);
+}
+
+CHESS_API int CHESS_CALL weights_snapshot(int* out, int out_len) {
+    const int need = 6 * 64 * 2 + FEATURE_NB;     /* mg + eg (PAWN..KING) + features = 776 */
+    if (!out || out_len < need) return CHESS_ERR_BUFFER;
+
+    std::lock_guard<std::mutex> lock(g_weightsMutex);
+    int n = 0;
+    for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
+        for (int sq = 0; sq < 64; ++sq) out[n++] = g_weights.mg[pt][sq];
+    for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
+        for (int sq = 0; sq < 64; ++sq) out[n++] = g_weights.eg[pt][sq];
+    for (int i = 0; i < FEATURE_NB; ++i) out[n++] = g_weights.featW[i];
+    return n;
+}
+
+CHESS_API TrainerHandle CHESS_CALL trainer_create(void) {
+    return new (std::nothrow) Trainer();
+}
+
+CHESS_API void CHESS_CALL trainer_record(TrainerHandle t, const char* fen) {
+    if (!t || !fen || !*fen) return;
+    ensure_initialized();
+
+    chess::Position pos = chess::Position::from_fen(fen);
+    double phase = game_phase(pos);
+
+    /* Per-square occupancy, split into midgame/endgame by phase, white-relative. */
+    chess::Bitboard occ = pos.pieces();
+    while (occ) {
+        chess::Square s  = chess::pop_lsb(occ);
+        chess::Piece  pc = pos.piece_on(s);
+        chess::Color  c  = chess::color_of(pc);
+        chess::PieceType pt = chess::type_of(pc);
+        int relSq = (c == chess::WHITE) ? int(s) : (int(s) ^ 56);
+        t->mgOcc[c][pt][relSq] += (1.0 - phase);
+        t->egOcc[c][pt][relSq] += phase;
+    }
+
+    /* Per-side feature activations. */
+    double w[FEATURE_NB], b[FEATURE_NB];
+    compute_features(pos, chess::WHITE, phase, w);
+    compute_features(pos, chess::BLACK, phase, b);
+    for (int i = 0; i < FEATURE_NB; ++i) {
+        t->featAcc[chess::WHITE][i] += w[i];
+        t->featAcc[chess::BLACK][i] += b[i];
+    }
+
+    t->plies++;
+}
+
+CHESS_API void CHESS_CALL trainer_apply(TrainerHandle t, int winner, double weight) {
+    if (!t) return;
+
+    std::lock_guard<std::mutex> lock(g_weightsMutex);
+
+    /* pass 0 = winner (reward, +1); pass 1 = loser (punish, -1). */
+    for (int pass = 0; pass < 2; ++pass) {
+        chess::Color side = chess::Color((pass == 0 ? winner : (winner ^ 1)) & 1);
+        int sign = pass == 0 ? 1 : -1;
+
+        for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
+            for (int sq = 0; sq < 64; ++sq) {
+                if (t->mgOcc[side][pt][sq] != 0.0) {
+                    int d = sign * int(std::lround(SQUARE_LR * t->mgOcc[side][pt][sq] * weight));
+                    g_weights.mg[pt][sq] = std::clamp(g_weights.mg[pt][sq] + d, -SQ_CLAMP, SQ_CLAMP);
+                }
+                if (t->egOcc[side][pt][sq] != 0.0) {
+                    int d = sign * int(std::lround(SQUARE_LR * t->egOcc[side][pt][sq] * weight));
+                    g_weights.eg[pt][sq] = std::clamp(g_weights.eg[pt][sq] + d, -SQ_CLAMP, SQ_CLAMP);
+                }
+            }
+
+        if (t->plies > 0)
+            for (int i = 0; i < FEATURE_NB; ++i) {
+                double avg = t->featAcc[side][i] / t->plies;        /* per-ply average, normalized */
+                int d = sign * int(std::lround(FEAT_LR * (avg / FEAT_SCALE[i]) * weight));
+                g_weights.featW[i] = std::clamp(g_weights.featW[i] + d, -FEAT_CLAMP, FEAT_CLAMP);
+            }
+    }
+
+    save_global_weights();
+}
+
+CHESS_API void CHESS_CALL trainer_destroy(TrainerHandle t) {
+    delete t;                                             /* delete nullptr is safe */
+}
+
 } /* extern "C" */

native/chess_engine/src/position.h

@@ -47,6 +47,22 @@ public:
     void do_move(Move m);
     void undo_move(Move m);
 
+    // Legal moves for a SPECIFIED color (for mobility eval of either side). When c is not
+    // the side to move, temporarily flips side-to-move (and clears the en-passant square,
+    // which belongs to the other side) so generate_legal runs for c, then restores. The
+    // Zobrist key is untouched and unused by move generation, so this leaves the position
+    // observably unchanged.
+    void generate_legal_for(Color c, MoveList& list) {
+        if (sideToMove == c) { generate_legal(list); return; }
+        Color  savedSide = sideToMove;
+        Square savedEp    = epSquare;
+        sideToMove = c;
+        epSquare   = SQ_NONE;
+        generate_legal(list);
+        sideToMove = savedSide;
+        epSquare   = savedEp;
+    }
+
     // Seed prior-position keys (oldest first, excluding the current position) so
     // is_draw() can see game history the FEN doesn't carry. Call once, right after
     // from_fen and before any do_move.