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Ephemeral Value Adjustments based on pfrl

figure

I implemented an algorithm called Ephemeral Value Adjustments (EVA) using pfrl.

Japanese README

EVA

Requirement

  • python >= 3.8
    • pfrl
    • OpenAI gym
      • atari-py >= 0.2.6

Usage

python train.py [options]

Options

  • --env : Environment name of the Atari.
    • Eg : --env PongNoFrameskip-v4
  • --gpu : If 0, use the GPU. If the number is less than 0, the GPU is not used.
  • --agent : Choose DQN and EVA.

TODO

  • Reduce GPU memory usage
  • More experiments.

Performance Comparison

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