A Python project implementing a Reinforcement Learning (Q-Learning) agent to play Rock, Paper, Scissors against various strategies.
- Q-Learning agent with configurable memory (last N moves)
- Multiple opponent strategies: random, pattern, counter, rock-lover, mixed, and human
- Interactive training and play modes
- Visualization of training progress and results
- Model saving and loading for persistent learning
- Strategy benchmarking and comparison