Planning with RL and episodic-memory behavioral priors

Shivansh Beohar; Andrew Melnik

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arXiv · 2207.01845

Planning with RL and episodic-memory behavioral priors

Shivansh Beohar·Andrew Melnik

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Abstract

The practical application of learning agents requires sample efficient and interpretable algorithms. Learning from behavioral priors is a promising way to bootstrap agents with a better-than-random exploration policy or a safe-guard against the pitfalls of early learning. Existing solutions for imitation learning require a large number of expert demonstrations and rely on hard-to-interpret learning methods like Deep Q-learning. In this work we present a planning-based approach that can use these behavioral priors for effective exploration and learning in a reinforcement learning environment, and we demonstrate that curated exploration policies in the form of behavioral priors can help an agent learn faster.

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Shivansh Beohar, Andrew Melnik. 2022-07-05. Planning with RL and episodic-memory behavioral priors. https://arxiv.org/abs/2207.01845

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