Deep Reinforcement Learning With Pytorch Save

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

Project README

Status: Active (under active development, breaking changes may occur)

This repository will implement the classic and state-of-the-art deep reinforcement learning algorithms. The aim of this repository is to provide clear pytorch code for people to learn the deep reinforcement learning algorithm.

In the future, more state-of-the-art algorithms will be added and the existing codes will also be maintained.



  • python <=3.6
  • tensorboardX
  • gym >= 0.10
  • pytorch >= 0.4

Note that tensorflow does not support python3.7


pip install -r requirements.txt

If you fail:

  • Install gym
pip install gym
  • Install the pytorch
please go to official webisite to install it:

Recommend use Anaconda Virtual Environment to manage your packages

  • Install tensorboardX
pip install tensorboardX
pip install tensorflow==1.12
  • Test
cd Char10\ TD3/
python --mode test

You could see a bipedalwalker if you install successfully.


    1. install openai-baselines (Optional)
# clone the openai baselines
git clone
cd baselines
pip install -e .


Here I uploaded two DQN models which is trianing CartPole-v0 and MountainCar-v0.

Tips for MountainCar-v0

This is a sparse binary reward task. Only when car reach the top of the mountain there is a none-zero reward. In genearal it may take 1e5 steps in stochastic policy. You can add a reward term, for example, to change to the current position of the Car is positively related. Of course, there is a more advanced approach that is inverse reinforcement learning.

step This is value loss for DQN, We can see that the loss increaded to 1e13, however, the network work well. Because the target_net and act_net are very different with the training process going on. The calculated loss cumulate large. The previous loss was small because the reward was very sparse, resulting in a small update of the two networks.

  1. Playing Atari with Deep Reinforcement Learning [arxiv] [code]
  2. Deep Reinforcement Learning with Double Q-learning [arxiv] [code]
  3. Dueling Network Architectures for Deep Reinforcement Learning [arxiv] [code]
  4. Prioritized Experience Replay [arxiv] [code]
  5. Noisy Networks for Exploration [arxiv] [code]
  6. A Distributional Perspective on Reinforcement Learning [arxiv] [code]
  7. Rainbow: Combining Improvements in Deep Reinforcement Learning [arxiv] [code]
  8. Distributional Reinforcement Learning with Quantile Regression [arxiv] [code]
  9. Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation [arxiv] [code]
  10. Neural Episodic Control [arxiv] [code]

Policy Gradient

Use the following command to run a saved model


Use the following command to train model



This is a model that I have trained.


This is an algorithmic framework, and the classic REINFORCE method is stored under Actor-Critic.


Episode reward in Pendulum-v0:




Advantage Policy Gradient, an paper in 2017 pointed out that the difference in performance between A2C and A3C is not obvious.

The Asynchronous Advantage Actor Critic method (A3C) has been very influential since the paper was published. The algorithm combines a few key ideas:

  • An updating scheme that operates on fixed-length segments of experience (say, 20 timesteps) and uses these segments to compute estimators of the returns and advantage function.
  • Architectures that share layers between the policy and value function.
  • Asynchronous updates.


Original paper:


This is not the implementation of the author of paper!!!

Episode reward in Pendulum-v0:



This is not the implementation of the author of paper!!!

Episode reward in Pendulum-v0:


Episode reward in BipedalWalker-v2:

If you want to use the test your model:

python --mode test

[01] A Brief Survey of Deep Reinforcement Learning
[02] The Beta Policy for Continuous Control Reinforcement Learning
[03] Playing Atari with Deep Reinforcement Learning
[04] Deep Reinforcement Learning with Double Q-learning
[05] Dueling Network Architectures for Deep Reinforcement Learning
[06] Continuous control with deep reinforcement learning
[07] Continuous Deep Q-Learning with Model-based Acceleration
[08] Asynchronous Methods for Deep Reinforcement Learning
[09] Trust Region Policy Optimization
[10] Proximal Policy Optimization Algorithms
[11] Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
[12] High-Dimensional Continuous Control Using Generalized Advantage Estimation
[13] Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
[14] Addressing Function Approximation Error in Actor-Critic Methods


  • DDPG
  • SAC
  • TD3

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