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Reinforcement Learning Algorithms

·2 mins
Reinforcement Learning Education Deep Learning PyTorch
Table of Contents

Comprehensive RL Algorithm Implementation #

Systematic implementation of all major reinforcement learning algorithms, ranging from classical value-based methods to modern policy gradient and actor-critic approaches. This project serves as both an educational resource and a research foundation for understanding RL algorithm design and behavior.

Algorithms Implemented #

Value-Based Methods #

  • DQN: Deep Q-Network with experience replay
  • Double DQN: Addressing overestimation bias
  • Dueling DQN: Separate value and advantage streams
  • Prioritized Experience Replay: Sampling importance for efficiency

Policy Gradient Methods #

  • REINFORCE: Monte Carlo policy gradient
  • Actor-Critic: Reducing variance with baseline estimation
  • A2C: Advantage Actor-Critic
  • A3C: Asynchronous gradient updates

Advanced Methods #

  • PPO: Proximal Policy Optimization
  • TRPO: Trust Region Policy Optimization
  • SAC: Soft Actor-Critic (off-policy maximum entropy)
  • TD3: Twin Delayed DDPG

Multi-Agent RL #

  • MADDPG: Multi-Agent Deep Deterministic Policy Gradient
  • Independent PPO: Multi-agent learning with shared policies

Technical Implementation #

Framework Design #

  • PyTorch-based implementations
  • Modular architecture for easy extension
  • Shared components (replay buffers, networks, trainers)
  • Unified experiment framework

Environments #

  • Classic control (CartPole, MountainCar, Acrobot)
  • Atari games (via gym)
  • Custom multi-agent environments
  • Consistent evaluation protocols

Experimentation #

  • TensorBoard logging
  • Hyperparameter sweeps
  • Comparative analysis tools
  • Reproducible random seeds

Educational Value #

Learning Resources #

  • Clean, documented code
  • Algorithm intuition through implementation
  • Comparative experiments showing tradeoffs
  • References to original papers

Research Applications #

  • Baseline implementations for new research
  • Ablation study framework
  • Quick prototyping of new ideas
  • Reproducible experimental setup

Technology Stack #

  • PyTorch for deep learning
  • OpenAI Gym/ Gymnasium for environments
  • NumPy for numerical operations
  • Matplotlib for visualization
  • TensorBoard for monitoring

Impact #

This project provides a foundation for understanding how different RL algorithms behave under various conditions. The systematic implementation approach reveals algorithmic nuances often overlooked in theoretical treatments, supporting both education and research efforts.