Reinforcement-Learning-Q-learning-Gridworld-Pytorch

This is a project using Pytorch to fulfill reinforcement learning on a simple game - Gridworld.

The basic introduction of this game is at:

Also, please refer to the Pytorch tutorial on Reinforcement Learning:

http://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html

Most of the game code and test code are copied from the game website. What I do is to use Pytorch rather than Keras to implemet the neural network of Q learning. Also, I have made some changes to make the code more "Pythonic". For instance, I replace the for loop in the experience replay to the vector calculation. This modification can speed up the running by parallel processing.

If you are interested in using Pytorch to create some programs, this game can be a good practice.

Simply run the main.py. The program will train the network and test it. The implementation of game is at gridworld.py and the implementation of Q learning is at DQN.py. Both of them have been imported into main.py already.

Here is the result after traning 1000 epoches.

Initial State:

[[' ' ' ' ' ' ' ']
 [' ' '-' '+' ' ']
 [' ' ' ' 'W' ' ']
 [' ' ' ' ' ' 'P']]
Variable containing:
 6.2493  4.8025  3.9134  4.7186
[torch.FloatTensor of size 1x4]

Move #: 0; Taking action: 0
[[' ' ' ' ' ' ' ']
 [' ' '-' '+' ' ']
 [' ' ' ' 'W' 'P']
 [' ' ' ' ' ' ' ']]
Variable containing:
 8.0619  4.3604  6.7725  6.0888
[torch.FloatTensor of size 1x4]

Move #: 1; Taking action: 0
[[' ' ' ' ' ' ' ']
 [' ' '-' '+' 'P']
 [' ' ' ' 'W' ' ']
 [' ' ' ' ' ' ' ']]
Variable containing:
  7.0695   5.9213  10.5555   7.7655
[torch.FloatTensor of size 1x4]

Move #: 2; Taking action: 2
[[' ' ' ' ' ' ' ']
 [' ' '-' ' ' ' ']
 [' ' ' ' 'W' ' ']
 [' ' ' ' ' ' ' ']]
Reward: 10

nickioan/Reinforcement-Learning-Q-learning-Gridworld-Pytorch

Reinforcement-Learning-Q-learning-Gridworld-Pytorch