Towards an Information Theoretic Framework of Context-Based Offline
Meta-Reinforcement Learning
- URL: http://arxiv.org/abs/2402.02429v1
- Date: Sun, 4 Feb 2024 09:58:42 GMT
- Title: Towards an Information Theoretic Framework of Context-Based Offline
Meta-Reinforcement Learning
- Authors: Lanqing Li, Hai Zhang, Xinyu Zhang, Shatong Zhu, Junqiao Zhao,
Pheng-Ann Heng
- Abstract summary: Context-based OMRL (COMRL) as a popular paradigm, aims to learn a universal policy conditioned on effective task representations.
We show that COMRL algorithms are essentially optimizing the same mutual information objective between the task variable $boldsymbolM$ and its latent representation $boldsymbolZ$ by implementing various approximate bounds.
Based on the theoretical insight and the information bottleneck principle, we arrive at a novel algorithm dubbed UNICORN, which exhibits remarkable generalization across a broad spectrum of RL benchmarks.
- Score: 50.976910714839065
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As a marriage between offline RL and meta-RL, the advent of offline
meta-reinforcement learning (OMRL) has shown great promise in enabling RL
agents to multi-task and quickly adapt while acquiring knowledge safely. Among
which, Context-based OMRL (COMRL) as a popular paradigm, aims to learn a
universal policy conditioned on effective task representations. In this work,
by examining several key milestones in the field of COMRL, we propose to
integrate these seemingly independent methodologies into a unified information
theoretic framework. Most importantly, we show that the pre-existing COMRL
algorithms are essentially optimizing the same mutual information objective
between the task variable $\boldsymbol{M}$ and its latent representation
$\boldsymbol{Z}$ by implementing various approximate bounds. Based on the
theoretical insight and the information bottleneck principle, we arrive at a
novel algorithm dubbed UNICORN, which exhibits remarkable generalization across
a broad spectrum of RL benchmarks, context shift scenarios, data qualities and
deep learning architectures, attaining the new state-of-the-art. We believe
that our framework could open up avenues for new optimality bounds and COMRL
algorithms.
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