Env from gym and compare different methods on two tasks: Mountain car v0 and Continuous mountain car v0
- Ubuntu
- Tensorflow 1.x (test on 1.12.0)
- Gym (test on 0.15.4)
- Numpy (test on 1.17.2)
- Matplotlib (test on 3.1.1)
# or if you use anaconda, you can directly run:
$ conda env create -f environment.ymlNotice if you want to render the video, in beginning of each file:
RENDER = True # Show GUIResult: DQN>Sarsa-lambda>Sarsa~QTable
$ cd MountainCar
# Run QTable
$ python QTable_MountainCar.py
# Run Sarsa
$ python Sarsa_MountainCar.py
# Run Sarsa-lambda
$ python Sarsalambda_MountainCar.py
# Run DQN
$ python DQN_MountainCar.pyResult: A2C>A3C
$ cd MountainCar_Continuous
# Run A2C
$ python A2C_MountainCar.py
# Run A3C
$ python A3C_MountainCar.py@misc{RLYixuan2020,
author = {Yixuan, Wei},
title = {Mountain car RL task},
howpublished = {\url{https://github.com/weiyx16/RL_Toy_Example}},
year = {2020}
}- https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow
- https://towardsdatascience.com/reinforcement-learning-w-keras-openai-dqns-1eed3a5338c
- https://github.com/stefanbo92/A3C-Continuous
- https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python
- https://medium.com/@asteinbach/actor-critic-using-deep-rl-continuous-mountain-car-in-tensorflow-4c1fb2110f7c
- https://towardsdatascience.com/understanding-actor-critic-methods-931b97b6df3f