Skip to content
Go to file

State-of-the-art Model-free Reinforcement Learning Algorithms Tweet

PyTorch and Tensorflow 2.0 implementation of state-of-the-art model-free reinforcement learning algorithms on both Openai gym environments and a self-implemented Reacher environment.

Algorithms include Soft Actor-Critic (SAC), Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt (including Cross-entropy (CE) Method), PointNet, Transporter, Recurrent Policy Gradient, Soft Decision Tree, etc.

Please note that this repo is more of a personal collection of algorithms I implemented and tested during my research and study period, rather than an official open-source library/package for usage. However, I think it could be helpful to share it with others and I'm expecting useful discussions on my implementations. But I didn't spend much time on cleaning or structuring the code. As you may notice that there may be several versions of implementation for each algorithm, I intentionally show all of them here for you to refer and compare. Also, this repo contains only PyTorch Implementation.

For official libraries of RL algorithms, I provided the following two with TensorFlow 2.0 + TensorLayer 2.0:

  • RL Tutorial (Status: Released) contains RL algorithms implementation as tutorials with simple structures.

  • RLzoo (Status: Released) is a baseline implementation with high-level API supporting a variety of popular environments, with more hierarchical structures for simple usage.

Since Tensorflow 2.0 has already incorporated the dynamic graph construction instead of the static one, it becomes a trivial work to transfer the RL code between TensorFlow and PyTorch.



python ***.py --train

python ***.py --test


If you meet problem "Not imlplemented Error", it may be due to the wrong gym version. The newest gym==0.14 won't work. Install gym==0.7 or gym==0.10 with pip install -r requirements.txt.


  • SAC for gym Pendulum-v0:

SAC with automatically updating variable alpha for entropy:

SAC without automatically updating variable alpha for entropy:

It shows that the automatic-entropy update helps the agent to learn faster.

  • TD3 for gym Pendulum-v0:

TD3 with deterministic policy:

TD3 with non-deterministic/stochastic policy:

It seems TD3 with deterministic policy works a little better, but basically similar.

  • AC for gym CartPole-v0:

However, vanilla AC/A2C cannot handle the continuous case like gym Pendulum-v0 well.


To cite this repository:

  author = {Zihan Ding},
  title = {SOTA-RL-Algorithms},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{}},


PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..





No releases published


No packages published