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Invited Talk
Workshop: Generalization in Planning (GenPlan '23)

Value-Based Abstractions for Planning

Amy Zhang

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Sat 16 Dec 7:25 a.m. PST — 8 a.m. PST


As reinforcement learning continues to advance, the integration of efficient planning algorithms with powerful representation learning becomes crucial for solving long-horizon tasks. We address key challenges in planning, reward learning, and representation learning through the objective of learning value-based abstractions. We explore this idea via goal-conditioned reinforcement learning to learn generalizable value functions and action-free pre-training. By leveraging self-supervised reinforcement learning and efficient planning algorithms, these approaches collectively contribute to the advancement of decision-making systems capable of learning and adapting to diverse tasks in real-world environments.

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