CODEWITHSUNDEEP
reinforcement-learningdqlq-learningdqn
corvo
corvo

Reputation: 724

Deep Q-Learning for grid world

Has anyone implemented the Deep Q-learning to solve a grid world problem where state is the [x, y] coordinates of the player and goal is to reach a certain coordinate [A, B]. Reward setting could be -1 for each step and +10 for reaching [A,B]. [A, B] is always fixed.

Surprisingly enough I did not find such an implementation on google. I tried DQN using taxi-v3 myself and it didn't work. So, looking for such a reference implementation to work my way up to my problem.

Upvotes: 0

Views: 1717

Answers (1)

Tom Dörr
Tom Dörr

Reputation: 1029

For grid worlds deep Q-learning isn't needed, that's probably why there are few people doing it. However I found a tutorial that uses deep Q-learning with a grid world: https://livebook.manning.com/book/deep-reinforcement-learning-in-action/chapter-3/1

Upvotes: 2

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