Developed an artificial intelligence agent that learns to play the classic PONG game using deep reinforcement learning techniques. The project demonstrates the power of neural networks in learning complex strategies through trial and error, similar to human learning processes.
Technical Implementation:
- Designed a Convolutional Neural Network architecture optimized for visual game state processing
- Implemented reinforcement learning algorithms using the policy gradient method
- Utilized OpenAI Gym environment for standardized game interactions and benchmarking
- Created comprehensive visualization tools using Matplotlib to track learning progress
Performance Results:
- Achieved 5% faster training convergence compared to popular online implementations
- Successfully trained the agent to consistently win against the built-in AI opponent
- Documented the entire research process with detailed explanations of RL concepts
Research Impact: Co-authored comprehensive documentation explaining reinforcement learning principles, making the complex concepts accessible to other researchers and students interested in AI game development.