Reinforcement Learning

Efficient Planning in a Compact Latent Action Space
Planning-based reinforcement learning has shown strong performance in tasks in discrete and low-dimensional continuous action spaces. However, scaling such methods to high-dimensional action spaces remains challenging. We propose Trajectory Autoencoding Planner (TAP), which learns a compact discrete latent action space from offline data for efficient planning, enabling continuous control in high-dimensional control with a learned model.
Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian
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AutoCAT: Reinforcement Learning for Automated Exploration of Cache-Timing Attacks
Security can be seen as a competitive game between an attacker and a defender RL can be used to automatically explore attacks (and defenses) on a black-box system
Oct 28, 2022 New Hampshire
Mulong Luo, Wenjie Xiong, Geunbae Lee, Yueying Li, Xiaomeng Yang, Amy Zhang, Yuandong Tian, Hsien-Hsin S. Lee, and Edward Suh
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AutoCAT: Reinforcement Learning for Automated Exploration of Cache-Timing Attacks
Efficient Planning in a Compact Latent Action Space
Planning-based reinforcement learning has shown strong performance in tasks in discrete and low-dimensional continuous action spaces. However, scaling such methods to high-dimensional action spaces remains challenging. We propose Trajectory Autoencoding Planner (TAP), which learns a compact discrete latent action space from offline data for efficient planning, enabling continuous control in high-dimensional control with a learned model.
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Efficient Planning in a Compact Latent Action Space