Provably Efficient Representation Selection in Low-rank Markov Decision
Processes: From Online to Offline RL
- URL: http://arxiv.org/abs/2106.11935v2
- Date: Wed, 14 Feb 2024 07:05:06 GMT
- Title: Provably Efficient Representation Selection in Low-rank Markov Decision
Processes: From Online to Offline RL
- Authors: Weitong Zhang and Jiafan He and Dongruo Zhou and Amy Zhang and
Quanquan Gu
- Abstract summary: We propose an efficient algorithm, called ReLEX, for representation learning in both online and offline reinforcement learning.
We show that the online version of ReLEX, called Re-UCB, always performs no worse than the state-of-the-art algorithm without representation selection.
For the offline counterpart, ReLEX-LCB, we show that the algorithm can find the optimal policy if the representation class can cover the state-action space.
- Score: 84.14947307790361
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The success of deep reinforcement learning (DRL) lies in its ability to learn
a representation that is well-suited for the exploration and exploitation task.
To understand how the choice of representation can improve the efficiency of
reinforcement learning (RL), we study representation selection for a class of
low-rank Markov Decision Processes (MDPs) where the transition kernel can be
represented in a bilinear form. We propose an efficient algorithm, called
ReLEX, for representation learning in both online and offline RL. Specifically,
we show that the online version of ReLEX, called ReLEX-UCB, always performs no
worse than the state-of-the-art algorithm without representation selection, and
achieves a strictly better constant regret if the representation function class
has a "coverage" property over the entire state-action space. For the offline
counterpart, ReLEX-LCB, we show that the algorithm can find the optimal policy
if the representation class can cover the state-action space and achieves
gap-dependent sample complexity. This is the first result with constant sample
complexity for representation learning in offline RL.
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