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.

• A2C
• ACER (A2C + Trust Region): It seems that this implementation has some problems ... (welcome bug report)
• CEM
• TRPO (TRPO single path)
• PPO (PPO clip)
• Vanilla PG

On the next stage, I want to implement

• DDPG
• Random Search (see Simple random search provides a competitive approach to reinforcement learning)
• SAC (soft actor-critic) with continuous action space
• SAC (soft actor-critic) with discrete action space
• DQN

Then next stage, discrete action space problem and raw video input (Atari) problems:

• Rainbow: DQN and relevant techniques (target network / double Q-learning / prioritized experience replay / dueling network structure / distributional RL)
• 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.