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Summary of Offline Reinforcement Learning with Behavioral Supervisor Tuning, by Padmanaba Srinivasan et al.


Offline Reinforcement Learning with Behavioral Supervisor Tuning

by Padmanaba Srinivasan, William Knottenbelt

First submitted to arxiv on: 25 Apr 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The abstract discusses offline reinforcement learning (RL) algorithms that learn performant, well-generalizing policies from static interaction datasets. Recent approaches have seen success but require hyperparameter tuning for each dataset, which can be cumbersome. The paper presents TD3 with Behavioral Supervisor Tuning (TD3-BST), an algorithm that trains uncertainty models to guide policy actions within the dataset support. TD3-BST learns more effective policies without per-dataset tuning and achieves best performance across challenging benchmarks.
Low GrooveSquid.com (original content) Low Difficulty Summary
Offline RL algorithms learn from static interaction datasets, but most approaches require hyperparameter tuning for each dataset. This can be time-consuming. The paper introduces TD3-BST, an algorithm that uses uncertainty models to guide policy actions within the dataset support. This allows for more effective policy learning without per-dataset tuning.

Keywords

» Artificial intelligence  » Hyperparameter  » Reinforcement learning