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A reinforcement learning agent that learns to play classic games such as Snake, Flappy Bird, or Atari titles from scratch, with visualisations of its learning progress.
Problem Statement
Reinforcement learning is hard to grasp from theory alone. Watching an agent improve at a game makes the concepts concrete.
Objectives
Build or integrate a game environment
Implement Deep Q-Network training
Compare algorithms and reward designs
Visualise learning curves and gameplay
Modules
Environment
Game wrapped with a standard state, action, and reward interface.
Agent
DQN with experience replay and target network.
Training Pipeline
Hyperparameter runs and checkpointing.
Visualisation
Reward curves and recorded gameplay.
Expected Output
A trained agent that plays the chosen game well, shown alongside videos of its progress from random play.
Future Scope
Policy gradient methods, multi-agent games, and transfer between games.