Model-based reinforcement learning for biological sequence design

[nagataka]

Summary

Link

Model-based reinforcement learning for biological sequence design

Author/Institution

Christof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, Lucy Colwell

What is this

Ref: Algorithm 1: DyNA PPO

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

Key points

Our method updates the policy’s parameters using sequences x generated by the current policy πθ(x), but evaluated using a learned surrogate f'(x), instead of the true, but unknown, oracle reward function f(x).

How the author proved effectiveness of the proposal?

Any discussions?

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