Microsoft Research Summit 2021
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Research talk: Successor feature sets: Generalizing successor representations across policies

Successor-style representations have many advantages for reinforcement learning. For example, they can help an agent generalize from experience to new goals. However, successor-style representations are not optimized to generalize across policies—typically, a limited-length list of policies is maintained and information shared among them by representation learning or generalized policy iteration. Join University of Maryland PhD candidate Kianté Brantley to address these limitations in successor-style representations. With collaborators from Microsoft Research Montréal, he developed a new general successor-style representation, which brings together ideas from predictive state representations, belief space value iteration, and convex analysis. The new representation is highly expressive. For example, it allows for efficiently reading off an optimal policy for a new reward function or a policy that imitates a demonstration. Together, you’ll explore the basics of successor-style representation, the challenges of current approaches, and results of the proposed approach on small, known environments.

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Track:
Reinforcement Learning
Date:
Speakers:
Kianté Brantley
Affiliation:
University of Maryland

Reinforcement Learning