PPO-exploration

Implementation of various exploration strategies for deep reinforcement learning.

Algorithms implemented:

• Proximal policy optomization (https://arxiv.org/abs/1707.06347)
• Similarity hashing with SimHash (https://arxiv.org/abs/1611.04717)
• Self supervised prediction with Intrinsic Curiosity Module(ICM) (https://arxiv.org/pdf/1705.05363.pdf)
• Random network distillation (RND) (https://arxiv.org/abs/1810.12894)
• Evolution Strategies based novelty search with Adaptive rewards (ES-NSRA) (https://arxiv.org/abs/1712.06560)

Requirements

• Python3
• PyTorch
• Numpy
• Mujoco_py
• Gym
• stable_baselines3

References for github repos used to aid creating this implementation

• https://github.com/DLR-RM/stable-baselines3
• https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail
• https://github.com/adik993/ppo-pytorch
• https://github.com/openai/random-network-distillation
• https://github.com/topics/random-network-distillation
• https://github.com/BY571/Deep-Reinforcement-Learning-Algorithm-Collection
• https://github.com/alirezamika/evostra

Performance