feat(PPO_Bot): deterministic eval + fix logStd warm-start

- actorForward: deterministic param, uses mean-only when PPOB_EVAL_ONLY=1
  (eval was adding unit Gaussian noise to every action — unreliable scores)
- warm_start.py: log_std initialized to -1.0 (std≈0.37) instead of copying
  snapshot values (were 2.27-4.68 → std 9-108, completely drowning signal)
- training.env: LOG_STD_CEILING 0.0→-0.5 (cap exploration at std≈0.6)

PPO_Bot/PPO_Bot.nim

@@ -281,7 +281,7 @@ method run(bot: PPOBot) =
     # detection-tick pulse, and the next iteration's spawn check sees false —
     # one shot → exactly one bullet, even across deadReckon gaps.
     bot.tracker.current.hasFired = false
-    let (rawActs, logP) = ac.actorForward(state)
+    let (rawActs, logP) = ac.actorForward(state, deterministic = evalOnly)
     let value = ac.criticForward(state)
     let ex = if bot.tracker.hasContact: bot.tracker.current.x else: botData.arenaWidth  / 2.0
     let ey = if bot.tracker.hasContact: bot.tracker.current.y else: botData.arenaHeight / 2.0

PPO_Bot/network.nim

@@ -43,9 +43,13 @@ proc forward*(mlp: MLP, x: Tensor[float32]): Tensor[float32] =
   let h2 = tanh(mlp.w2 * h1 + mlp.b2)
   result  = mlp.w3 * h2 + mlp.b3
 
-proc actorForward*(ac: ActorCritic, state: Tensor[float32]): tuple[actions: Tensor[float32], logProb: float32] =
-  ## state: [STATE_DIM]. Returns sampled actions [ACTION_DIM] and sum log-prob.
+proc actorForward*(ac: ActorCritic, state: Tensor[float32], deterministic = false): tuple[actions: Tensor[float32], logProb: float32] =
+  ## state: [STATE_DIM]. Returns actions [ACTION_DIM] and sum log-prob.
+  ## deterministic=true: return mean only (no noise), logProb=0.
   let mean = ac.actor.forward(state)
+  if deterministic:
+    return (actions: mean, logProb: 0.0'f32)
+
   # Floor logStd at logStdFloor before exp → min std ≈ exp(logStdFloor)
   var clampedLogStd = ac.logStd.map(proc(v: float32): float32 = clamp(v, logStdFloor, logStdCeiling))
   let std = clampedLogStd.map(proc(v: float32): float32 = exp(v))