PyTorch Programming

  • Implementing reinforcement learning agents with PyTorch

  • Designing states, actions, and reward functions for puzzle environments

  • Training Deep Q-Learning agents

  • Applying exploration strategies such as ε-greedy policies and decay schedules

  • Using replay buffers and target networks

  • Evaluating and comparing RL agents

  • Performing systematic hyperparameter optimization