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Implementation of Deep Mind's Paper titled: "Human Level Control through Deep Reinforcement Learning"

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DeepQLearning

Implementation of Deep Mind's Paper titled: "Human Level Control through Deep Reinforcement Learning". The policy gradient algorithm was also implemented and tested on Cart Pole just for experiments.

Requirements

  • Python 3.6.1
  • Tensorflow
  • Gym
  • numpy
  • sklearn
  • matplotlib

Training the model

  • For DQN python main.py When prompted to select train, play or visualize type 'train'
  • For Policy Gradients python policy_gradients.py When prompted to select train or play type 'train'

Testing the model

  • For DQN python main.py When prompted to select train, play or visualize type 'play'
  • For Policy Gradients python policy_gradients.py When prompted to select train or play type 'play'

Results

Game Highest Score
Breakout 45
Cart Pole 200

Breakout

alt text

Cartpole

alt text

Experiments

Breakout

alt text

Cartpole

alt text

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Implementation of Deep Mind's Paper titled: "Human Level Control through Deep Reinforcement Learning"

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