PyTorch CartPole Example

Simple Cartpole example writed with pytorch.

Why Cartpole?

Cartpole is very easy problem and is converged very fast in many case. So you can run this example in your computer(maybe it take just only 1~2 minitue).

Rainbow

• DQN [1]
• Double [2]
• Duel [3]
• Multi-step [4]
• PER(Prioritized Experience Replay) [5]
• Nosiy-Net [6]
• Distributional(C51) [7]
• Rainbow [8]

PG(Policy Gradient)

• REINFORCE [9]
• Actor Critic [10]
• Advantage Actor Critic
• GAE(Generalized Advantage Estimation) [12]
• TNPG [20]
• TRPO [13]
• PPO - Single Version [14]

Parallel

• Asynchronous Q-learning [11]
• A3C (Asynchronous Advantage Actor Critic) [11]
• ACER [21]
• PPO [14]
• APE-X DQN [15]
• IMPALA [23]
• R2D2 [16]

Distributional DQN

• QRDQN [18]
• IQN [19]

Exploration

• ICM [22]
• RND [17]

POMDP (With RNN)

• DQN (use state stack)
• DRQN [24] [25]
• DRQN (use state stack)
• DRQN (store Rnn State) [16]
• R2D2 - Single Version [16]

Reference

[1]Playing Atari with Deep Reinforcement Learning
[2]Deep Reinforcement Learning with Double Q-learning
[3]Dueling Network Architectures for Deep Reinforcement Learning
[4]Reinforcement Learning: An Introduction
[5]Prioritized Experience Replay
[6]Noisy Networks for Exploration
[7]A Distributional Perspective on Reinforcement Learning
[8]Rainbow: Combining Improvements in Deep Reinforcement Learning
[9]Policy Gradient Methods for Reinforcement Learning with Function Approximation
[10]Actor-Critic Algorithms
[11]Asynchronous Methods for Deep Reinforcement Learning
[12]HIGH-DIMENSIONAL CONTINUOUS CONTROL USING GENERALIZED ADVANTAGE ESTIMATION
[13]Trust Region Policy Optimization
[14]Proximal Policy Optimization
[15]DISTRIBUTED PRIORITIZED EXPERIENCE REPLAY
[16]RECURRENT EXPERIENCE REPLAY IN DISTRIBUTED REINFORCEMENT LEARNING
[17]EXPLORATION BY RANDOM NETWORK DISTILLATION
[18]Distributional Reinforcement Learning with Quantile Regression
[19]Implicit Quantile Networks for Distributional Reinforcement Learning
[20]A Natural Policy Gradient
[21]SAMPLE EFFICIENT ACTOR-CRITIC WITH EXPERIENCE REPLAY
[22]Curiosity-driven Exploration by Self-supervised Prediction
[23]IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
[24]Deep Recurrent Q-Learning for Partially Observable MDPs
[25]Playing FPS Games with Deep Reinforcement Learning

Acknowledgements

• https://github.com/openai/baselines
• https://github.com/reinforcement-learning-kr/pg_travel
• https://github.com/reinforcement-learning-kr/distributional_rl
• https://github.com/Kaixhin/Rainbow
• https://github.com/Kaixhin/ACER
• https://github.com/higgsfield/RL-Adventure-2

Use Cuda

check this issue. https://github.com/g6ling/Reinforcement-Learning-Pytorch-Cartpole/issues/1