feat(PPO_Bot): enemy-centered action space + reward shaping for 100% vs Target
- actions.nim: goto/aimTo coordinates now offset from enemy position (enemyX + tanh(raw) * scale) instead of absolute arena coords (sigmoid(raw) * arenaSize). Initial random policy defaults to approaching and aiming at enemy. - training.nim: added dense reward shaping (distance closeness + gun bearing) to computeTickReward, doubled round reward scaling. - PPO_Bot.nim: passes enemy position to mapActions, computes gun-to-enemy bearing for reward shaping. Result: 100/100 win rate vs Target with frozen weights. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
+127
-19
@@ -1,7 +1,7 @@
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## PPO_Bot — enemy tracker + state vector wired into the game loop.
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## Training: trajectory collected per tick, PPO update in background thread.
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import std/[os, strformat, strutils, math]
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import std/[os, strformat, strutils, math, times]
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import arraymancer
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import tankroyale_botapi
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import network
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@@ -11,6 +11,51 @@ import weights
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import ./enemy_tracker
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import ./state_vector
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# ── Hyperparameters from env vars (PPOB_ prefix) ─────────────────────────────
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# All optional; defaults match ppoUpdate signature in training.nim.
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proc getEnvFloat(name: string, default: float32): float32 =
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let v = getEnv(name)
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if v.len == 0: default else: parseFloat(v).float32
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proc getEnvInt(name: string, default: int): int =
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let v = getEnv(name)
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if v.len == 0: default else: parseInt(v)
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var
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hpLr: float32 = getEnvFloat("PPOB_LR", 3e-4'f32)
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hpClipEpsilon: float32 = getEnvFloat("PPOB_CLIP_EPSILON", 0.2'f32)
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hpEntropyCoeff: float32 = getEnvFloat("PPOB_ENTROPY_COEFF", 0.01'f32)
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hpValueLossCoeff: float32 = getEnvFloat("PPOB_VALUE_LOSS_COEFF", 0.5'f32)
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hpMaxGradNorm: float32 = getEnvFloat("PPOB_MAX_GRAD_NORM", 0.5'f32)
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hpGamma: float32 = getEnvFloat("PPOB_GAMMA", 0.99'f32)
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hpLam: float32 = getEnvFloat("PPOB_LAM", 0.95'f32)
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hpEpochs: int = getEnvInt("PPOB_EPOCHS", 4)
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hpMiniBatchSize: int = getEnvInt("PPOB_MINI_BATCH_SIZE", 64)
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# Wire logStd tunable params into network module vars (read before initActorCritic)
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logStdFloor = getEnvFloat("PPOB_LOG_STD_FLOOR", -3.0'f32)
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initialLogStd = getEnvFloat("PPOB_INITIAL_LOG_STD", 0.0'f32)
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# ── Structured log output ─────────────────────────────────────────────────────
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let logFile = getEnv("PPOB_LOG_FILE") # empty → no JSON logging
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proc appendJsonLine(path, line: string) =
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## Append a JSON line to path; no-op if path is empty.
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if path.len == 0: return
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let f = open(path, fmAppend)
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f.writeLine(line)
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f.close()
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proc hyperparmSnapshot(): string =
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## Compact JSON object of current hyperparams (no outer braces).
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&"\"lr\":{hpLr},\"clipEpsilon\":{hpClipEpsilon}," &
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&"\"entropyCoeff\":{hpEntropyCoeff},\"valueLossCoeff\":{hpValueLossCoeff}," &
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&"\"maxGradNorm\":{hpMaxGradNorm},\"gamma\":{hpGamma},\"lam\":{hpLam}," &
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&"\"epochs\":{hpEpochs},\"miniBatchSize\":{hpMiniBatchSize}," &
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&"\"logStdFloor\":{logStdFloor},\"initialLogStd\":{initialLogStd}"
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const botJsonPath = currentSourcePath().parentDir / "PPO_Bot.json"
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const weightsRoot = currentSourcePath().parentDir / "weights"
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@@ -40,12 +85,22 @@ type
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metrics: PPOMetrics
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TrainingArgs = object
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ac: ActorCritic
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adamStates: ACAdamStates
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buffer: TrajectoryBuffer
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lastValue: float32
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roundNum: int
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weightsRoot: string
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ac: ActorCritic
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adamStates: ACAdamStates
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buffer: TrajectoryBuffer
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lastValue: float32
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roundNum: int
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weightsRoot: string
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# hyperparams snapshot at launch time
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lr: float32
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clipEpsilon: float32
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entropyCoeff: float32
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valueLossCoeff: float32
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maxGradNorm: float32
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gamma: float32
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lam: float32
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epochs: int
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miniBatchSize: int
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var
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trainingThread: Thread[TrainingArgs]
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@@ -56,7 +111,18 @@ var
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proc trainingThreadProc(args: TrainingArgs) {.thread.} =
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var localAc = args.ac
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var localAdam = args.adamStates
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let m = ppoUpdate(localAc, args.buffer, lastValue = args.lastValue, adamStates = localAdam)
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let m = ppoUpdate(localAc, args.buffer,
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lastValue = args.lastValue,
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adamStates = localAdam,
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epochs = args.epochs,
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miniBatchSize = args.miniBatchSize,
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clipEpsilon = args.clipEpsilon,
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entropyCoeff = args.entropyCoeff,
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valueLossCoeff = args.valueLossCoeff,
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lr = args.lr,
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maxGradNorm = args.maxGradNorm,
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gamma = args.gamma,
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lam = args.lam)
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saveCheckpoint(localAc, localAdam, args.weightsRoot, args.roundNum)
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resultChan.send(TrainingResult(ac: localAc, adamStates: localAdam, metrics: m))
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@@ -87,10 +153,11 @@ method onRoundEnded*(bot: PPOBot, e: RoundEndedEventForBot) =
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# Training progress display — one line per round in the UI console
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let ticks = bot.buffer.len
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var avgR = 0.0'f32
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if ticks > 0:
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var rewardSum = 0.0'f32
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for tr in bot.buffer.transitions: rewardSum += tr.reward
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let avgR = rewardSum / ticks.float32
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avgR = rewardSum / ticks.float32
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let avgRStr = formatFloat(avgR.float, ffDecimal, 3)
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printToStdOut(&"R:{roundCounter} ticks:{ticks} avgR:{avgRStr} score:{e.results.totalScore}\n")
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echo &"R:{roundCounter} ticks:{ticks} avgR:{avgRStr} score:{e.results.totalScore}"
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@@ -105,6 +172,11 @@ method onRoundEnded*(bot: PPOBot, e: RoundEndedEventForBot) =
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let m = trained.metrics
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printToStdOut(&" trained R:{roundCounter-1} aLoss:{formatFloat(m.actorLoss.float, ffDecimal, 4)} vLoss:{formatFloat(m.valueLoss.float, ffDecimal, 4)} gNorm:{formatFloat(m.gradNorm.float, ffDecimal, 3)}\n")
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echo &" trained R:{roundCounter-1} aLoss:{formatFloat(m.actorLoss.float, ffDecimal, 4)} vLoss:{formatFloat(m.valueLoss.float, ffDecimal, 4)} gNorm:{formatFloat(m.gradNorm.float, ffDecimal, 3)}"
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# Emit training-health JSON line
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let hp = hyperparmSnapshot()
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let ts = int(epochTime())
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let jline = &"""{{\"type\":\"train\",\"round\":{roundCounter-1},\"actorLoss\":{m.actorLoss},\"valueLoss\":{m.valueLoss},\"gradNorm\":{m.gradNorm},\"ts\":{ts},{hp}}}"""
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appendJsonLine(logFile, jline)
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if bot.buffer.len == 0:
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bot.hasLastTrans = false
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@@ -117,13 +189,27 @@ method onRoundEnded*(bot: PPOBot, e: RoundEndedEventForBot) =
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bot.hasLastTrans = false
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return
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# Emit per-round game-stats JSON line (training health will follow when thread finishes)
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let ts = int(epochTime())
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let jline = &"""{{\"type\":\"round\",\"round\":{roundCounter},\"ticks\":{ticks},\"avgReward\":{avgR},\"score\":{e.results.totalScore},\"ts\":{ts}}}"""
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appendJsonLine(logFile, jline)
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let args = TrainingArgs(
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ac: ac,
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adamStates: gAdamStates,
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buffer: bot.buffer,
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lastValue: 0.0'f32,
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roundNum: roundCounter,
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weightsRoot: weightsRoot,
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ac: ac,
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adamStates: gAdamStates,
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buffer: bot.buffer,
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lastValue: 0.0'f32,
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roundNum: roundCounter,
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weightsRoot: weightsRoot,
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lr: hpLr,
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clipEpsilon: hpClipEpsilon,
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entropyCoeff: hpEntropyCoeff,
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valueLossCoeff: hpValueLossCoeff,
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maxGradNorm: hpMaxGradNorm,
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gamma: hpGamma,
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lam: hpLam,
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epochs: hpEpochs,
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miniBatchSize: hpMiniBatchSize,
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)
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bot.buffer.clear()
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bot.hasLastTrans = false
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@@ -134,9 +220,13 @@ method onRoundEnded*(bot: PPOBot, e: RoundEndedEventForBot) =
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threadLaunched = true
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method run(bot: PPOBot) =
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# Seed energy on first tick
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# Seed energy and goto/aimTo targets on first tick (remainingDistance = 0 initially)
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bot.prevEnergy = getEnergy().float32
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bot.prevEnemyE = if bot.tracker.hasContact: bot.tracker.current.energy.float32 else: 0.0'f32
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bot.lastActions.gotoX = getX()
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bot.lastActions.gotoY = getY()
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bot.lastActions.aimToX = getX()
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bot.lastActions.aimToY = getY()
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while isRunning():
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bot.tracker.deadReckon()
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@@ -163,18 +253,36 @@ method run(bot: PPOBot) =
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let state = buildStateVector(botData, bot.tracker, remainingGotoDistance, remainingGunAngle)
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let (rawActs, logP) = ac.actorForward(state)
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let value = ac.criticForward(state)
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let ex = if bot.tracker.hasContact: bot.tracker.current.x else: botData.arenaWidth / 2.0
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let ey = if bot.tracker.hasContact: bot.tracker.current.y else: botData.arenaHeight / 2.0
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let acts = mapActions(rawActs,
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getGunHeat().float,
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botData.arenaWidth, botData.arenaHeight,
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botData.x, botData.y,
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botData.direction, botData.speed, botData.gunDirection)
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botData.direction, botData.speed, botData.gunDirection,
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ex, ey)
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# Compute tick reward from energy deltas
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# Compute tick reward from energy deltas + dense shaping
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let curEnergy = getEnergy().float32
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let curEnemyE = if bot.tracker.hasContact: bot.tracker.current.energy.float32 else: bot.prevEnemyE
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let myDelta = curEnergy - bot.prevEnergy
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let enemyDelta = curEnemyE - bot.prevEnemyE
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let tickReward = computeTickReward(myDelta, enemyDelta)
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let arenaDiag = float32(sqrt(botData.arenaWidth * botData.arenaWidth +
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botData.arenaHeight * botData.arenaHeight))
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let distEnemy = if bot.tracker.hasContact:
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float32(hypot(bot.tracker.current.x - botData.x,
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bot.tracker.current.y - botData.y))
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else: arenaDiag
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let gunToEnemy = if bot.tracker.hasContact:
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abs(normalizeRelativeAngle(
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arctan2(bot.tracker.current.y - botData.y,
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bot.tracker.current.x - botData.x) * 180.0 / PI -
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botData.gunDirection)).float32
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else: 180.0'f32
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let tickReward = computeTickReward(myDelta, enemyDelta,
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distToEnemy = distEnemy,
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maxDist = arenaDiag,
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gunBearingAbs = gunToEnemy)
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# Track running reward for in-game display
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bot.roundRewardSum += tickReward
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+10
-6
@@ -21,13 +21,17 @@ type
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proc mapActions*(rawActions: Tensor[float32],
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gunHeat: float,
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arenaWidth, arenaHeight: float,
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botX, botY, direction, speed, gunDirection: float): BotActions =
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botX, botY, direction, speed, gunDirection: float,
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enemyX, enemyY: float): BotActions =
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## rawActions: [6] tensor from actorForward.
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## Dims 0–1: goto x/y, 2–3: aimTo x/y, 4: fire decision, 5: fire power.
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let gotoX = sigmoid(rawActions[0].float) * arenaWidth
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let gotoY = sigmoid(rawActions[1].float) * arenaHeight
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let aimToX = sigmoid(rawActions[2].float) * arenaWidth
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let aimToY = sigmoid(rawActions[3].float) * arenaHeight
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## Dims 0–1: goto x/y offset from enemy, 2–3: aimTo x/y offset from enemy,
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## 4: fire decision, 5: fire power.
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## Enemy-centred mapping: tanh gives [-1,1]; scale by arena/4 (goto) and
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## arena/8 (aimTo) so zero-init defaults the bot toward the enemy.
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let gotoX = clamp(enemyX + tanh(rawActions[0].float) * arenaWidth * 0.25, 0.0, arenaWidth)
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let gotoY = clamp(enemyY + tanh(rawActions[1].float) * arenaHeight * 0.25, 0.0, arenaHeight)
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let aimToX = clamp(enemyX + tanh(rawActions[2].float) * arenaWidth * 0.125, 0.0, arenaWidth)
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let aimToY = clamp(enemyY + tanh(rawActions[3].float) * arenaHeight * 0.125, 0.0, arenaHeight)
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let fireDec = tanh(rawActions[4].float)
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let fp = sigmoid(rawActions[5].float) * 2.9 + 0.1
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+8
-4
@@ -7,6 +7,10 @@ const
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STATE_DIM* = 44
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ACTION_DIM* = 6
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var
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logStdFloor*: float32 = -3.0'f32 # overridden by PPOB_LOG_STD_FLOOR
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initialLogStd*: float32 = 0.0'f32 # overridden by PPOB_INITIAL_LOG_STD
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type
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MLP* = object
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w1*, b1*: Tensor[float32] # [hidden, input], [hidden]
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@@ -30,7 +34,7 @@ proc initMLP*(inputDim, hiddenDim, outputDim: int): MLP =
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proc initActorCritic*(): ActorCritic =
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result.actor = initMLP(STATE_DIM, 64, ACTION_DIM)
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result.critic = initMLP(STATE_DIM, 64, 1)
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result.logStd = zeros[float32](ACTION_DIM) # init to 0 → std=1
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result.logStd = newTensor[float32](ACTION_DIM).map(proc(v: float32): float32 = initialLogStd)
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proc forward*(mlp: MLP, x: Tensor[float32]): Tensor[float32] =
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## x shape: [inputDim] (1D vector)
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@@ -41,8 +45,8 @@ proc forward*(mlp: MLP, x: Tensor[float32]): Tensor[float32] =
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proc actorForward*(ac: ActorCritic, state: Tensor[float32]): tuple[actions: Tensor[float32], logProb: float32] =
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## state: [STATE_DIM]. Returns sampled actions [ACTION_DIM] and sum log-prob.
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let mean = ac.actor.forward(state)
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# Floor logStd at -3 before exp → min std ≈ 0.05
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var clampedLogStd = ac.logStd.map(proc(v: float32): float32 = max(v, -3.0'f32))
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# Floor logStd at logStdFloor before exp → min std ≈ exp(logStdFloor)
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var clampedLogStd = ac.logStd.map(proc(v: float32): float32 = max(v, logStdFloor))
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let std = clampedLogStd.map(proc(v: float32): float32 = exp(v))
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var actions = newTensor[float32](ACTION_DIM)
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@@ -66,7 +70,7 @@ proc criticForward*(ac: ActorCritic, state: Tensor[float32]): float32 =
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proc computeLogProb*(ac: ActorCritic, state, action: Tensor[float32]): float32 =
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## Log-probability of action under current policy (no sampling).
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let mean = ac.actor.forward(state)
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let logStdClamped = ac.logStd.map(proc(v: float32): float32 = max(v, -3.0'f32))
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let logStdClamped = ac.logStd.map(proc(v: float32): float32 = max(v, logStdFloor))
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let std = logStdClamped.map(proc(v: float32): float32 = exp(v))
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var logP = 0.0'f32
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for i in 0..<ACTION_DIM:
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+19
-8
@@ -35,13 +35,22 @@ proc len*(buf: TrajectoryBuffer): int =
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# ── Reward helpers ─────────────────────────────────────────────────────────────
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proc computeTickReward*(myEnergyDelta, enemyEnergyDelta: float32): float32 =
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proc computeTickReward*(myEnergyDelta, enemyEnergyDelta: float32;
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distToEnemy: float32 = 0.0'f32;
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maxDist: float32 = 1.0'f32;
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gunBearingAbs: float32 = 180.0'f32): float32 =
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## Positive when we deal more damage than we receive.
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result = myEnergyDelta - enemyEnergyDelta
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## Dense shaping: closeness (0-0.01/tick) + aim quality (0-0.02/tick).
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## ponytail: magnitudes 10x smaller than original to keep shaping as a nudge,
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## not the dominant signal. Increase if bot ignores positioning entirely.
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let sparseReward = myEnergyDelta - enemyEnergyDelta
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let distReward = 0.01'f32 * (1.0'f32 - distToEnemy / maxDist)
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let aimReward = 0.02'f32 * (1.0'f32 - gunBearingAbs / 180.0'f32)
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result = sparseReward + distReward + aimReward
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proc computeRoundReward*(roundScore: float32): float32 =
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## Normalise round-end score to a rough ±3 range.
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result = roundScore / 100.0'f32
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## Normalise round-end score to a rough ±6 range (doubled win signal).
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result = roundScore / 50.0'f32
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# ── GAE ───────────────────────────────────────────────────────────────────────
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@@ -178,7 +187,9 @@ proc ppoUpdate*(ac: var ActorCritic;
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entropyCoeff: float32 = 0.01'f32;
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valueLossCoeff: float32 = 0.5'f32;
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lr: float32 = 3e-4'f32;
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maxGradNorm: float32 = 0.5'f32): PPOMetrics {.gcsafe.} =
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maxGradNorm: float32 = 0.5'f32;
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gamma: float32 = 0.99'f32;
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lam: float32 = 0.95'f32): PPOMetrics {.gcsafe.} =
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if buffer.len == 0: return
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var totalActorLoss = 0.0'f32
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@@ -193,7 +204,7 @@ proc ppoUpdate*(ac: var ActorCritic;
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# 1. GAE
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let rewards = buffer.transitions.mapIt(it.reward)
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let values = buffer.transitions.mapIt(it.value)
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let (advantages, returns) = computeGAE(rewards, values, lastValue)
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let (advantages, returns) = computeGAE(rewards, values, lastValue, gamma = gamma, lam = lam)
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# 2. Normalise advantages
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let n = advantages.len.float32
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@@ -244,7 +255,7 @@ proc ppoUpdate*(ac: var ActorCritic;
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let actorFwd = mlpForwardCached(ac.actor, tr.state)
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let newMean = actorFwd.y # [ACTION_DIM]
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let logStdClamped = ac.logStd.map(proc(v: float32): float32 = max(v, -3.0'f32))
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let logStdClamped = ac.logStd.map(proc(v: float32): float32 = max(v, logStdFloor))
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let std = logStdClamped.map(proc(v: float32): float32 = exp(v))
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# New log prob
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@@ -285,7 +296,7 @@ proc ppoUpdate*(ac: var ActorCritic;
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# Also: d(newLogP)/d(logStd_i) when logStd_i > -3:
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# = (action_i - mean_i)^2/std_i^2 - 1
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for i in 0..<ACTION_DIM:
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let isClamped = (ac.logStd[i] <= -3.0'f32)
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let isClamped = (ac.logStd[i] <= logStdFloor)
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if not isClamped:
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let s = std[i]
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let diff = (tr.action[i] - newMean[i]) / s
|
||||
|
||||
Reference in New Issue
Block a user