Model-Based Reinforcement Learning for Atari

[nagataka]

Summary

Link

• Model-Based Reinforcement Learning for Atari
• https://openreview.net/forum?id=S1xCPJHtDB

Author/Institution

What is this

• Simulated Policy Learning (SimPLe)
  • Video prediction techniques + policy training within the learned model
• Look like Dyna-style World models

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Comparison with previous researches. What are the novelties/good points?

Key points

• a skip-connected convolutional encoder and decoder, which outputs the next predicted frame and expected reward
• a convolutional inference network which approximates the posterior given the next frame
• LSTM based network, which is trained to approximate each bit given the previous ones

How the author proved effectiveness of the proposal?

• Experiments on Atari games
  • with a budget restricted to 100K time steps – roughly to two hours of a play time
  • outperforms state-of-the-art model-free algorithms (Rainbow)

Any discussions?

What should I read next?