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Reinforcement-Learning-Agents

Play various of games using deep learning Tensorflow and deep Evolution Strategies.

All agents will be trained on RGB frames and feature values native from the game engine.

Right now I got machine limitation, so I am very sorry for late result posting.

Do not try any of these code yet, I never tested it before, still in development.

ES == Evolution Strategies
DL == Deep Learning

Every games will be developed on different algorithms:

  1. Reward based {ES}
  2. Policy gradient {ES, DL}
  3. Q-learning {ES, DL}
  4. Double Q-learning {ES, DL}
  5. Recurrent-Q-learning {DL}
  6. Double Recurrent-Q-learning {DL}
  7. Dueling Q-learning {DL}
  8. Dueling Recurrent-Q-learning {DL}
  9. Double Dueling Q-learning {DL}
  10. Double Dueling Recurrent-Q-learning {DL}
  11. Actor-Critic {DL}
  12. Actor-Critic Dueling {DL}
  13. Actor-Critic Recurrent {DL}
  14. Actor-Critic Dueling Recurrent {DL}

Games that will be developed:

  1. Flappy bird
  2. level 1 mario
  3. pong
  4. Catcher
  5. Pixelcopter
  6. Raycast Maze
  7. Snake
  8. Water World
  9. Dooms

How to read the file, game-folder/algorithm/{features, frames}_{DL, ES}.py

This repository will be update overtime.

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Play various of games using deep learning Tensorflow and deep Evolution Strategies

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  • Python 65.3%
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