Action Space
Action space, in reinforcement learning, refers to the set of all possible actions an agent can take within an environment. Current research focuses on efficiently handling large or complex action spaces, particularly in multi-agent systems and continuous control problems, employing techniques like action discretization, factorization, and the use of large language models for guidance. These advancements are crucial for scaling reinforcement learning to real-world applications, such as robotics and resource management, where high-dimensional and nuanced action choices are common. Improved methods for handling action spaces directly impact the sample efficiency and overall performance of reinforcement learning algorithms.
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Papers
March 9, 2025
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February 20, 2025
An Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces
Alexander Terenin, Jeffrey NegreaCornell University●University of Waterloo●Vector InstituteMaking Universal Policies Universal
Niklas Höpner, David Kuric, Herke van HoofUniversity of Amsterdam