Implementing Deep Reinforcement Learning Algorithms in Python for use in the MuJoCo Physics Simulator

DQN-family reinforcement learning project for Gymnasium Pendulum-v1 with action discretisation, configs, notebooks, tests, and MLOps docs.

This project investigates to what extend evolutionary methods such as the Cross Entropy Method and Evolution Strategies can be used to optimize a neural policy compared to the baseline REINFORCE.

Training an agent in the gym Pendulum-v1 environment using Actor-Critic algorithm.

Continuous Environment

Contains Expert Trajectories for various Gym Environments used for State Only Imitation Learning