• The purpose of this repository is study and research about reinforcement learning for robotics control.

• This repository provides the Model-Free reinforcement learning algorithms.

DDPG
TRPO
PPO
PPO2
SAC
TD3

• These algorithms are demonstrated in Environment Reacher with ML-Agent.

• The directory architecture have to be under format.

└─spinning_up_kr
   ├─env(environment of reacher in unity)
   ├─mlagents
   ├─buffer.py
   ├─core.py
   ├─ddpg.py
   ├─ou_noise.py
   ├─ppo.py
   ├─ppo2.py
   ├─sac.py
   ├─td3.py
   └─trpo.py

Reference

[1] Proximal Policy Optimization

[2] High-Dimensional Continuous Control Using Generalized Advantage Estimation

[3] Continuous Control With Deep Reinforcement Learning

[4] OpenAI Spinningup

[5] Reinforcement Learning Korea PG Travel

[6] Medipixel Reinforcement Learning Repository

[7] Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

[8] tensorflow reinforcement learning framework

[9] Trust Region Policy Optimization

[10] Addressing Function Approximation Error in Actor-Critic Methods