RL-Algorithms

This deep reinforcement learning library provides an easy way to implement and experiment with deep RL algoirthms.

Written in PyTorch, the core module includes the infrastructure behind most algoirthms like

• Abstract base classes for agents
• Easy instantiation of MLP
• Q-Functions, Value Functions
• Various Policies (Gaussian, Deterministic, Categorical)
• Replay Buffer and GAE Buffer
• MLP Dynamics Model (WIP)

Currently implemented algorithms include:

• Tabular Q-Learning
• VPG (Vanilla Policy Gradient)
• PPO (Proximal Policy Optimization)
• DDPG (Deep Deterministic Policy Gradient)
• TD3 (Twin Delayed DDPG)

WIP:

• Model-Based MPC
• Model-Ensemble PPO