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

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. Code and videos are available at http: //sites. google. com/view/qss-paper.

Authors

• Ashley D. Edwards
• Himanshu Sahni
• Rosanne Liu
• Jane Hung
• Ankit Jain
• Rui Wang 0052
• Adrien Ecoffet
• Thomas Miconi
• Charles Isbell
• Jason Yosinski

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