On Context Distribution Shift in Task Representation Learning for
Offline Meta RL
- URL: http://arxiv.org/abs/2304.00354v2
- Date: Tue, 23 May 2023 13:14:57 GMT
- Title: On Context Distribution Shift in Task Representation Learning for
Offline Meta RL
- Authors: Chenyang Zhao, Zihao Zhou, Bin Liu
- Abstract summary: We focus on context-based OMRL, specifically on the challenge of learning task representation for OMRL.
To overcome this problem, we present a hard-sampling-based strategy to train a robust task context encoder.
- Score: 7.8317653074640186
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Offline Meta Reinforcement Learning (OMRL) aims to learn transferable
knowledge from offline datasets to enhance the learning process for new target
tasks. Context-based Reinforcement Learning (RL) adopts a context encoder to
expediently adapt the agent to new tasks by inferring the task representation,
and then adjusting the policy based on this inferred representation. In this
work, we focus on context-based OMRL, specifically on the challenge of learning
task representation for OMRL. We conduct experiments that demonstrate that the
context encoder trained on offline datasets might encounter distribution shift
between the contexts used for training and testing. To overcome this problem,
we present a hard-sampling-based strategy to train a robust task context
encoder. Our experimental findings on diverse continuous control tasks reveal
that utilizing our approach yields more robust task representations and better
testing performance in terms of accumulated returns compared to baseline
methods. Our code is available at https://github.com/ZJLAB-AMMI/HS-OMRL.
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