PPO Atari Pong RAM Artifacts

This file visualizes the training curve produced by the optional Pong RAM PPO module in NN/Examples/Models/RL/PPOPongRam.lean.

Pong RAM uses the same Gymnasium boundary as CartPole, with ALE registration and a higher-dimensional observation. The viewer focuses on saved-artifact inspection for the Atari RAM path rather than benchmark-specific PPO tuning.

Dependency setup for the optional ALE path:

python3 -m pip install --user 'gymnasium>=1.0' ale-py

After producing a log, put the cursor on the command below in an editor. The infoview will render the saved log.

Notes:

• The executable writes data/rl/ppo_pong_ram_trainlog.json by default (override with --log).
• This viewer is pure: if the file is missing, it shows an error panel instead of failing to build.

References:

• Schulman et al., "Proximal Policy Optimization Algorithms" (2017): https://arxiv.org/abs/1707.06347
• Machado et al., "Revisiting the Arcade Learning Environment" (2018): https://arxiv.org/abs/1709.06009
• ALE docs: https://ale.farama.org/