Some exploration scripts and notebooks into RL world with OpenAI/gym and Keras or Pytorch. Keras-RL is also explored along with my proper DQN implementation.
The toy example environment chosen is the Taxi-v3 for its simplicity and the possibility to work directly with a finite length Q-table.
Usefull references
- https://towardsdatascience.com/reinforcement-learning-lets-teach-a-taxi-cab-how-to-drive-4fd1a0d00529
- https://www.learndatasci.com/tutorials/reinforcement-q-learning-scratch-python-openai-gym/
- https://levelup.gitconnected.com/build-a-taxi-driving-agent-in-a-post-apocalyptic-world-using-reinforcement-learning-machine-175b1edd8f69
- https://medium.com/@anirbans17/reinforcement-learning-for-taxi-v2-edd7c5b76869
- https://towardsdatascience.com/reinforcement-learning-explained-visually-part-4-q-learning-step-by-step-b65efb731d3e
- https://towardsdatascience.com/reinforcement-learning-explained-visually-part-5-deep-q-networks-step-by-step-5a5317197f4b