Simple Cartpole example writed with pytorch.
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).
- DQN [1]
- Double [2]
- Duel [3]
- Multi-step [4]
- PER(Prioritized Experience Replay) [5]
- Nosiy-Net [6]
- Distributional(C51) [7]
- Rainbow [8]
- REINFORCE [9]
- Actor Critic [10]
- Advantage Actor Critic
- GAE(Generalized Advantage Estimation) [12]
- TNPG [20]
- TRPO [13]
- PPO [14]
- ACER [21]
[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