Optimistic Active Exploration of Dynamical Systems
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Author / Producer
Date
2024-07
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Reinforcement learning algorithms commonly seek to optimize policies for solving one particular task. How should we explore an unknown dynamical system such that the estimated model allows us to solve multiple downstream tasks in a zero-shot manner? In this paper, we address this challenge, by developing an algorithm -- OPAX -- for active exploration. OPAX uses well-calibrated probabilistic models to quantify the epistemic uncertainty about the unknown dynamics. It optimistically---w.r.t. to plausible dynamics---maximizes the information gain between the unknown dynamics and state observations. We show how the resulting optimization problem can be reduced to an optimal control problem that can be solved at each episode using standard approaches. We analyze our algorithm for general models, and, in the case of Gaussian process dynamics, we give a sample complexity bound andshow that the epistemic uncertainty converges to zero. In our experiments, we compare OPAX with other heuristic active exploration approaches on several environments. Our experiments show that OPAX is not only theoretically sound but also performs well for zero-shot planning on novel downstream tasks.
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Publication status
published
Book title
Advances in Neural Information Processing Systems 36
Journal / series
Volume
Pages / Article No.
38122 - 38153
Publisher
Curran
Event
37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03908 - Krause, Andreas / Krause, Andreas
09620 - Coros, Stelian / Coros, Stelian
Notes
Poster presentation on December 12, 2023.
Funding
180545 - NCCR Automation (phase I) (SNF)