An autonomous reinforcement learning agent developed in PyTorch that masters the game of Flappy Bird through Deep Q-Networks (DQN), reward engineering, and continuous exploration-exploitation tuning.

Key Highlights & Features

• State Representation & Action Space: Designed a 12-dimensional continuous state vector capturing critical kinematic metrics (player velocity, vertical distance to pipes, gap distances, next pipe coordinates) paired with a 2-action discrete action space (Flap or Do Nothing).
• Deep Q-Network Architecture: Implemented a multi-layer deep neural network with target-network synchronization to stabilize Q-value estimation and prevent policy divergence.
• Experience Replay & Exploration: Utilized prioritized experience replay buffer to break correlation between sequential frames and applied an epsilon-greedy decay strategy for effective exploration.
• Training Pipeline & Observability: Integrated reward tracking, loss monitoring, model checkpointing, and customizable hyperparameters via configuration files for iterative experimentation.

Tech Stack

• Frameworks & Libraries: PyTorch, Gymnasium, NumPy, Matplotlib
• RL Techniques: Deep Q-Networks (DQN), Experience Replay, Target Networks, Epsilon-Greedy Exploration