Pac-Man AI Simulator
5 DQN agents trained to play Pac-Man using deep reinforcement learning.
Problem Statement
Classic Pac-Man requires navigating a maze, eating pellets, evading ghosts, and maximizing score — a rich testbed for reinforcement learning agents. The challenge: train agents that generalize strategy rather than memorize paths.
Technical Approach
Implemented 5 distinct DQN agents, each with different architectural improvements:
- Baseline DQN — vanilla DQN with experience replay and target network
- Double DQN — reduces Q-value overestimation by decoupling action selection from evaluation
- Dueling DQN — separates state-value and advantage streams for more efficient learning
- Prioritized Replay DQN — samples high-error transitions more frequently
- Rainbow DQN — combines all improvements into a single agent
The game environment was built from scratch with a custom engine, feeding pixel-based observations into a shared CNN backbone.
Results
| Agent | Avg Score | Best Score | |---|---|---| | Baseline DQN | 1,240 | 3,800 | | Double DQN | 1,890 | 5,200 | | Dueling DQN | 2,340 | 6,100 | | Prioritized DQN | 2,710 | 7,400 | | Rainbow | 3,450 | 9,200 |
Rainbow achieved human-comparable performance after 2M training steps.