/RL-Algorithms-by-Pytorch

Clean and robust implementations of Reinforcement Learning algorithms by Pytorch

RL-Algorithms-by-Pytorch

I found the current implementations of Reinforcement Learning Algorithms are somewhat complicated, which is hard to get start.

Here are some classical Reinforcement Learning Algorithms implemented by Pytorch. I tried to make them clean, robust, and unified, hoping to help you get start with RL quickly.

Now I have finished DQN, DDQN, PPO discrete, PPO continuous, TD3, SAC Continuous. I will implement more in the future.

DQN/DDQN:

PPO Discrete:

PPO Continuous:

TD3:

SAC Continuous:

References

DQN: Mnih V , Kavukcuoglu K , Silver D , et al. Playing Atari with Deep Reinforcement Learning[J]. Computer Science, 2013.

Double DQN: Hasselt H V , Guez A , Silver D . Deep Reinforcement Learning with Double Q-learning[J]. Computer ence, 2015.

PPO: Proximal Policy Optimization Algorithms, Emergence of Locomotion Behaviours in Rich Environments

TD3: Fujimoto S , Hoof H V , Meger D . Addressing Function Approximation Error in Actor-Critic Methods[J]. 2018.

SAC: Soft Actor-Critic Algorithms and Applications