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
