DQN MSPacman Agents

Built reinforcement learning agents for Ms. Pac-Man using the OpenAI Gym environment. Started with a baseline Deep Q-Network (DQN) and extended it with Double DQN, Upper Confidence Bound (UCB) exploration, and a dueling architecture. Systematically tuned hyperparameters and replay buffer strategies to stabilise training. The best-performing agent, the agent with UCB plus the dueling architecture achieved the highest episodic scores and outperformed the baseline vanilla DQN baseline. Beyond raw scores the different techniques produced agents with different behaviours, by studying these differences I was able to tune the agents in order to improve performance.