vietnh1009/Super-mario-bros-PPO-pytorch
Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
Implements a deep reinforcement learning agent using PyTorch with an actor-critic architecture, achieving 31/32 level completion across the full Super Mario Bros NES game by leveraging PPO's clipped objective function for stable policy updates. Integrates with the OpenAI Gym environment wrapper for NES emulation and employs CNN feature extraction from raw pixel inputs paired with separate policy and value networks. Training supports per-level hyperparameter tuning and Docker containerization for reproducible GPU-accelerated training workflows.
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Jul 24, 2021
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