New chapter for hard-exploration Atari. PPO with Random Network
Distillation (Burda et al., 2018) as the curiosity bonus.

- env.py: ALE/MontezumaRevenge-v5 (and pitfall, private_eye) with the
  standard Atari preprocessing, no FireResetEnv, no LifeLossTerminalEnv
  (uninterrupted episodes so intrinsic returns can chain across deaths).
- 1-ppo-rnd.py: two-value-head ActorCritic, RND target/predictor with
  LeakyReLU, single-frame normalized input clipped to [-5, 5], obs RMS
  seeded by 50 rollouts of a random agent, intrinsic reward scaled by
  running std of discounted intrinsic returns, dual GAE (extrinsic
  episodic + intrinsic non-episodic), predictor updated on 25% of each
  minibatch, combined advantage A = 2*A_ext + 1*A_int.

Not run end-to-end yet. Sanity-checked static shapes and module wiring.

PPO+RND made reproducible and resumable. Shared run plumbing (seed,
metrics.jsonl, periodic/milestone/best checkpoints, resume, final summary)
lives in env.py's RunLogger, keeping the algorithm file focused. 512 parallel
envs crack the first-key bottleneck (128 envs never scored in 50M); final mean
per-game return ~3120 @ 65M steps, single seed (M4 Max), above the paper PPO
baseline (2497). Adds a README benchmark row. Count-based exploration is
deferred to a later PR (not yet trained/benchmarked).