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A Deep Q Learning framework to train an agent to play simple board games w/o the help of CNNs
Python
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figs
games
models
utils
weights
.gitignore
README.md
cart-player.py
connectn-player.py
requirements.txt
snake-player.py
tictactoe-player.py

README.md

Deep Q Learning

TODO

  • add pong and snake games
  • pull the step and state logc out of the game(s)
  • support for AI vs AI
  • optimize the model, other archtectures (ongoing)
  • port the model to browser. (tfjs?)
  • browser to snap
    • save the weights
    • load the weights
    • easy definitoin of actions, state and rewards
  • reward clipping
  • more games: frogger, catching games, pong, openai games, grid world
  • checkout games from qlearning4k

Resources

  • Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013)
  • Different game environemtns form OpenAi's Gym
  • Base DQN link
  • Base TicTacToe game link
  • Base Snake game gist
  • Base Connectn game gist
  • Base Pong game github repo
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