Jakub Šlambora Pocuston
Pocuston
/ Cartpole-v0.py
Last active
May 10, 2021 02:25
Solution of Open AI gym environment "Cartpole-v0" (https://gym.openai.com/envs/CartPole-v0) using DQN and Pytorch. It is is slightly modified version of Pytorch DQN tutorial from http://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html. For results see: https://gym.openai.com/evaluations/eval_KYrmuUX4TWGOWYsJl8i6Kg.
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| # Solution of Open AI gym environment "Cartpole-v0" (https://gym.openai.com/envs/CartPole-v0) using DQN and Pytorch. | |
| # It is is slightly modified version of Pytorch DQN tutorial from | |
| # http://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html. | |
| # The main difference is that it does not take rendered screen as input but it simply uses observation values from the \ | |
| # environment. | |
| import gym | |
| from gym import wrappers | |
| import random | |
| import math |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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| # Solution of Open AI gym environment "Cartpole-v0" (https://gym.openai.com/envs/CartPole-v0) using DQN and Pytorch. | |
| # It is is slightly modified version of Pytorch DQN tutorial from | |
| # http://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html. | |
| # The main difference is that it does not take rendered screen as input but it simply uses observation values from the \ | |
| # environment. | |
| import gym | |
| from gym import wrappers | |
| import random | |
| import math |