[2303.02135] Eventual Discounting Temporal Logic Counterfactual Experience Replay
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Computer Science > Machine Learning

arXiv:2303.02135 (cs)
[Submitted on 3 Mar 2023]

Title:Eventual Discounting Temporal Logic Counterfactual Experience Replay

Authors:Cameron Voloshin, Abhinav Verma, Yisong Yue
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Abstract:Linear temporal logic (LTL) offers a simplified way of specifying tasks for policy optimization that may otherwise be difficult to describe with scalar reward functions. However, the standard RL framework can be too myopic to find maximally LTL satisfying policies. This paper makes two contributions. First, we develop a new value-function based proxy, using a technique we call eventual discounting, under which one can find policies that satisfy the LTL specification with highest achievable probability. Second, we develop a new experience replay method for generating off-policy data from on-policy rollouts via counterfactual reasoning on different ways of satisfying the LTL specification. Our experiments, conducted in both discrete and continuous state-action spaces, confirm the effectiveness of our counterfactual experience replay approach.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO); Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2303.02135 [cs.LG]
  (or arXiv:2303.02135v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2303.02135

Submission history

From: Cameron Voloshin [view email]
[v1] Fri, 3 Mar 2023 18:29:47 UTC (3,943 KB)
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