DeepSacrifice

environments/hack0/server/agents/attacker_agent.ts

@@ -0,0 +1,46 @@
+import { Chess } from "chess.js";
+
+export function getAggressiveMove(
+  fen: string,
+): { from: string; to: string } | null {
+  const chess = new Chess(fen);
+  const moves = chess.moves({ verbose: true });
+  if (moves.length === 0) return null;
+
+  // Prefer captures
+  const captures = moves.filter(
+    (m) => m.flags.includes("c") || m.flags.includes("e"),
+  );
+  if (captures.length > 0) {
+    const move = captures[Math.floor(Math.random() * captures.length)];
+    return { from: move.from, to: move.to };
+  }
+
+  // Prefer checks
+  const checks = moves.filter((m) => {
+    chess.move({ from: m.from, to: m.to });
+    const isCheck = chess.inCheck();
+    chess.undo();
+    return isCheck;
+  });
+  if (checks.length > 0) {
+    const move = checks[Math.floor(Math.random() * checks.length)];
+    return { from: move.from, to: move.to };
+  }
+
+  // Otherwise, pick random
+  const move = moves[Math.floor(Math.random() * moves.length)];
+  return { from: move.from, to: move.to };
+}
+
+// Minimal AttackerAgent class with placeholder learning
+export class AttackerAgent {
+  getMove(fen: string): { from: string; to: string } | null {
+    return getAggressiveMove(fen);
+  }
+
+  learnFromGame(gameData: any, llmFeedback: any): void {
+    // TODO: Implement incremental learning from LLM feedback
+    // For now, this is a placeholder
+  }
+}