基准RL算法的实现_Python_Shell_下载.zip

# Reinforcement-Implementation This project aims to reproduce the results of several model-free RL algorithms in continuous action domain (mujuco environment). This projects * uses pytorch package * implements different algorithms independently in seperate files / minimal files * is written in simplest style * tries to follow the original paper and reproduce their results My first stage of work is to reproduce this figure in the PPO paper.  - [x] A2C - [x] ACER (A2C + Trust Region): It seems that this implementation has some problems ... (welcome bug report) - [X] CEM - [x] TRPO (TRPO single path) - [x] PPO (PPO clip) - [x] Vanilla PG On the next stage, I want to implement - [ ] DDPG - [X] Random Search (see [Simple random search provides a competitive approach to reinforcement learning](https://arxiv.org/pdf/1803.07055.pdf)) - [ ] SAC (soft actor-critic) with continuous action space - [X] SAC (soft actor-critic) with discrete action space - [X] DQN Then next stage, discrete action space problem and raw video input (Atari) problems: - [X] Rainbow: DQN and relevant techniques (target network / double Q-learning / prioritized experience replay / dueling network structure / distributional RL) - [X] PPO with random network distillation (RND) Rainbow on Atari with only 3M: It works but may need further tuning.  And then model-based algorithms (not planned) - [ ] PILCO - [ ] PE-TS TODOs: - [ ] change the way reward counts, current way may underestimate the reward (evaluate a deterministic model rather a stochastic/exploratory model) ## PPO Implementation PPO implementation is of high quality - matches the performance of openai.baselines. ## Update Recently, I added Rainbow and DQN. The Rainbow implementation is of high quality on Atari games - enough for you to modify and write your own research paper. The DQN implementation is a minimum workaround and reaches a good performance on MountainCar (which is a simple task but many codes on Github do not achieve good performance or need additional reward/environment engineering). This is enough for you to have a fast test of your research ideas.