Estimating Q(s,s') with Deep Deterministic Dynamics Gradients

TL;DR

A new Q funciton for off-policy reinforcement learning that doesn't rely on actions.

Abstract

In this paper, we introduce a novel form of value function, Q(s,s′), that expresses the utility of transitioning from a state s to a neighboring state s′ and then acting optimally thereafter. In order to derive an optimal policy, we develop a forward dynamics model that learns to make next-state predictions that maximize this value. This formulation decouples actions from values while still learning off-policy. We highlight the benefits of this approach in terms of value function transfer, learning within redundant action spaces, and learning off-policy from state observations generated by sub-optimal or completely random policies.

BibTeX
@article{edwards2020estimating,
  title={Estimating Q(s,s’) with Deep Deterministic Dynamics Gradients},
  author={Ashley D. Edwards and Himanshu Sahni and Rosanne Liu and Jane Hung and Ankit Jain and Rui Wang and Adrien Ecoffet and Thomas Miconi and Charles Isbell and Jason Yosinski},
  year={2020},
  eprint={2002.09505},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}
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