Reinforcement Learning Algorithms

Collection of general RL algorithms as a training exercise. Work in progress.
Reinforcement learning is a type of machine learning that is concerned with teaching agents how to make decisions in an environment. The agent learns to achieve a goal in an uncertain, potentially complex environment.

Do not run any of these on your CPU.

Deep Deterministic Policy Gradient (DDPG)

https://arxiv.org/abs/1509.02971

DDPG is an off-policy, model-free reinforcement learning algorithm that combines deterministic policy gradient methods with experience replays and actor-critic networks. It is used for continuous action spaces.

Used in this project to solve Lunar Lander problem from Gymnasium (similar to the deprecated OpenAI Gym) which is considered solved at +200 score. This model was able to achieve a 100 episode running average of +200 at 891 episodes.

Requires Python3, PyTorch, NumPy, Matplotlib and Gymnasium. Ensure compatibility between versions.

Soft Actor-Critic (SAC)

https://arxiv.org/abs/1801.01290

TODO

Proximal Policy Optimization (PPO)

https://arxiv.org/abs/1707.06347

TODO

Multi-Agent Deep Deterministic Policy Gradient (MADDPG)

https://arxiv.org/abs/1706.02275

TODO

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://arxiv.org/abs/1803.11485

TODO