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package commanderailab.ml;
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import commanderailab.ai.AiPolicy;
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import commanderailab.schema.DecisionSnapshot;
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import java.util.*;
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/**
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* LearnedPolicy — AI policy backed by a trained neural network.
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*
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* Instead of using Forge's built-in heuristic AI, this policy queries
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* the Python policy server (via PolicyServerClient) for macro-action
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* decisions at each game decision point.
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*
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* The policy server runs the trained PolicyNetwork:
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* game state → state encoder → neural network → macro-action
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*
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* If the policy server is unavailable, falls back to Forge's built-in AI.
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*
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* Usage:
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* LearnedPolicy policy = new LearnedPolicy("http://localhost:8080");
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* policy.setPlaystyle("aggro");
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* policy.setGreedy(true);
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*
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* // In the game loop:
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* DecisionSnapshot state = captureGameState();
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* String macroAction = policy.decideAction(state);
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* MacroActionExecutor.ActionResolution action =
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* MacroActionExecutor.resolve(macroAction, hand, battlefield, ...);
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*
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* Integration with BatchRunner:
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* When usesForgeBuiltinAi() returns false, the BatchRunner should
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* intercept decisions and call decideAction() instead. For the
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* initial version (Phase 7), this works alongside Forge's AI —
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* the policy's decision is logged but Forge still executes the game.
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* In Phase 8+, decisions will be fully intercepted.
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*/
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public class LearnedPolicy implements AiPolicy {
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private final PolicyServerClient client;
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private String playstyle;
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private boolean greedy;
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private boolean policyServerAvailable;
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// Stats
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private int totalQueries = 0;
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private int successfulQueries = 0;
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private int fallbackQueries = 0;
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private long totalInferenceMs = 0;
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// Cache the last prediction for logging
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private PolicyServerClient.PolicyPrediction lastPrediction;
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/**
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* Create a learned policy connected to the lab API server.
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*
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* @param serverUrl Base URL (e.g., "http://localhost:8080")
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*/
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public LearnedPolicy(String serverUrl) {
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this.client = new PolicyServerClient(serverUrl);
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this.playstyle = "midrange";
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this.greedy = true;
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this.policyServerAvailable = false;
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}
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/**
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* Create with explicit settings.
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*/
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public LearnedPolicy(String serverUrl, String playstyle, boolean greedy) {
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this(serverUrl);
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this.playstyle = playstyle;
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this.greedy = greedy;
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}
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// ══════════════════════════════════════════════════════════
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// Connection
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// ══════════════════════════════════════════════════════════
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/**
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* Check if the policy server has a trained model loaded.
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* Call this at startup to decide whether to use learned policy.
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*/
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public boolean connect() {
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policyServerAvailable = client.isAvailable();
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if (policyServerAvailable) {
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System.out.println("[ML] Learned policy connected — server has model loaded");
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} else {
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System.out.println("[ML] Learned policy not available: " + client.getLastError());
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System.out.println("[ML] Falling back to Forge built-in AI");
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}
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return policyServerAvailable;
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}
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/**
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* Request the server to reload the model (after training).
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*/
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public boolean reloadModel() {
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boolean ok = client.reloadModel();
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if (ok) {
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policyServerAvailable = true;
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System.out.println("[ML] Model reloaded successfully");
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}
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return ok;
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}
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// ══════════════════════════════════════════════════════════
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// Decision Making
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// ══════════════════════════════════════════════════════════
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/**
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* Query the policy network for a macro-action decision.
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*
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* @param snapshot Current game state snapshot
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* @return Macro-action name (e.g., "cast_creature"), or "pass" on failure
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*/
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public String decideAction(DecisionSnapshot snapshot) {
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totalQueries++;
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if (!policyServerAvailable) {
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fallbackQueries++;
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return MacroActionExecutor.PASS;
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}
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PolicyServerClient.PolicyPrediction pred = client.predict(snapshot, playstyle, greedy);
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lastPrediction = pred;
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if (pred.isValid()) {
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successfulQueries++;
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totalInferenceMs += (long) pred.inferenceMs;
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return pred.action;
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} else {
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fallbackQueries++;
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System.err.println("[ML] Prediction failed: " + pred.error);
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return MacroActionExecutor.PASS;
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}
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}
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/**
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* Get the full prediction result for the last decision.
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* Useful for logging probabilities alongside game actions.
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*/
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public PolicyServerClient.PolicyPrediction getLastPrediction() {
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return lastPrediction;
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}
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/**
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* Resolve the last decision to a concrete action.
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* Convenience method that combines decideAction + MacroActionExecutor.
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*/
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public MacroActionExecutor.ActionResolution resolveAction(
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DecisionSnapshot snapshot,
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List<String> hand,
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List<String> battlefield,
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List<String> commandZone,
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int manaAvailable,
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String commanderName,
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int commanderTax
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) {
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String macroAction = decideAction(snapshot);
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return MacroActionExecutor.resolve(
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macroAction, hand, battlefield, commandZone,
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manaAvailable, commanderName, commanderTax
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);
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}
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// ══════════════════════════════════════════════════════════
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// AiPolicy Interface
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// ══════════════════════════════════════════════════════════
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@Override
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public String getName() {
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return "learned-policy";
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}
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@Override
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public double getCardAdvantageWeight() {
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// These weights are not used by the learned policy
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// (the network learns its own weights)
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return 0.5;
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}
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@Override
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public double getTempoWeight() {
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return 0.5;
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}
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@Override
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public double getThreatScoringWeight() {
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return 0.5;
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}
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@Override
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public double getCombatWeight() {
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return 0.5;
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}
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@Override
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public boolean usesForgeBuiltinAi() {
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// When policy server is not available, fall back to Forge AI
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return !policyServerAvailable;
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}
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// ══════════════════════════════════════════════════════════
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// Configuration
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// ══════════════════════════════════════════════════════════
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public void setPlaystyle(String playstyle) {
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this.playstyle = playstyle;
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}
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public String getPlaystyle() {
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return playstyle;
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}
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public void setGreedy(boolean greedy) {
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this.greedy = greedy;
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}
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public boolean isGreedy() {
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return greedy;
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}
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public boolean isPolicyServerAvailable() {
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return policyServerAvailable;
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}
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// ══════════════════════════════════════════════════════════
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// Stats
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// ══════════════════════════════════════════════════════════
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public int getTotalQueries() { return totalQueries; }
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public int getSuccessfulQueries() { return successfulQueries; }
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public int getFallbackQueries() { return fallbackQueries; }
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public double getAverageInferenceMs() {
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return successfulQueries > 0 ? (double) totalInferenceMs / successfulQueries : 0;
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}
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public double getSuccessRate() {
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return totalQueries > 0 ? (double) successfulQueries / totalQueries : 0;
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}
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/**
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* Get a summary of policy performance for logging.
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*/
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public String getStatsSummary() {
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return String.format(
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"LearnedPolicy[queries=%d, success=%d (%.1f%%), fallback=%d, avg_inference=%.1fms, style=%s, greedy=%s]",
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totalQueries, successfulQueries,
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getSuccessRate() * 100,
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fallbackQueries, getAverageInferenceMs(),
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playstyle, greedy
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);
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}
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public void resetStats() {
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totalQueries = 0;
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successfulQueries = 0;
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fallbackQueries = 0;
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totalInferenceMs = 0;
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}
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@Override
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public String toString() {
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return String.format("LearnedPolicy[server=%s, available=%s, style=%s]",
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policyServerAvailable ? "connected" : "disconnected",
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policyServerAvailable, playstyle);
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}
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}
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