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Efficient Planning in a Compact Latent Action Space
zhengyao Jiang · Tianjun Zhang · Michael Janner · Yueying (Lisa) Li · Tim Rocktäschel · Edward Grefenstette · Yuandong Tian

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in 3rd Offline Reinforcement Learning Workshop: Offline RL as a "Launchpad" »
Event URL: https://openreview.net/forum?id=pVBETTS2av »

Planning-based reinforcement learning has shown strong performance in tasks in discrete and low-dimensional continuous action spaces.However, planning usually brings significant computational overhead for decision making, so scaling such methods to high-dimensional action spaces remains challenging. To advance efficient planning for high-dimensional continuous control, we propose Trajectory Autoencoding Planner (TAP), which learns low-dimensional latent action codes from offline data. The decoder of the VQ-VAE thus serves as a novel dynamics model that takes latent actions and current state as input and reconstructs long-horizon trajectories. During inference time, given a starting state, TAP searches over discrete latent actions to find trajectories that have both high probability under the training distribution and high predicted cumulative reward. Empirical evaluation in the offline RL setting demonstrates low decision latency which is indifferent to the growing raw action dimensionality. For Adroit robotic hand manipulation tasks with high-dimensional continuous action space, TAP surpasses existing model-based methods by a large margin and also beats strong model-free actor-critic baselines.

Author Information

zhengyao Jiang (University College London)
Tianjun Zhang (University of California, Berkeley)
Michael Janner (UC Berkeley)
Yueying (Lisa) Li (Cornell)
Tim Rocktäschel (University College London, Facebook AI Research)

Tim is a Researcher at Facebook AI Research (FAIR) London, an Associate Professor at the Centre for Artificial Intelligence in the Department of Computer Science at University College London (UCL), and a Scholar of the European Laboratory for Learning and Intelligent Systems (ELLIS). Prior to that, he was a Postdoctoral Researcher in Reinforcement Learning at the University of Oxford, a Junior Research Fellow in Computer Science at Jesus College, and a Stipendiary Lecturer in Computer Science at Hertford College. Tim obtained his Ph.D. from UCL under the supervision of Sebastian Riedel, and he was awarded a Microsoft Research Ph.D. Scholarship in 2013 and a Google Ph.D. Fellowship in 2017. His work focuses on reinforcement learning in open-ended environments that require intrinsically motivated agents capable of transferring commonsense, world and domain knowledge in order to systematically generalize to novel situations.

Edward Grefenstette (Cohere & University College London)
Yuandong Tian (Facebook AI Research)

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