mlsanityData to 14 Oct 2026

Automatic Trade-off Adaptation in Offline RL

arXiv 2306.09744 ↗First posted 16 Jun 2023cs.LGcited by 0

On the map: Offline RL and imitation learning from data → Offline RL algorithms

Abstract (arXiv)

Recently, offline RL algorithms have been proposed that remain adaptive at runtime. For example, the LION algorithm citelion provides the user with an interface to set the trade-off between behavior cloning and optimality w.r.t. the estimated return at runtime. Experts can then use this interface to adapt the policy behavior according to their preferences and find a good trade-off between conservatism and performance optimization. Since expert time is precious, we extend the methodology with an autopilot that automatically finds the correct parameterization of the trade-off, yielding a new algorithm which we term AutoLION.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data. Builds on 6 of its 9 references that are in the map. First posted on arXiv 16 Jun 2023.

Builds on

6 of 9 references.

Closest ancestors · 5

Same direction, newest first

  1. 2022Confidence-Conditioned Value Functions for Offline Reinforcement Learning8 Dec
  2. 2022Offline RL Policies Should be Trained to be Adaptive5 Jul
  3. 2022User-Interactive Offline Reinforcement Learning21 May
  4. 2022RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning26 Apr
  5. 2020Overcoming Model Bias for Robust Offline Deep Reinforcement Learning12 Aug

Other references · 1

  1. 2017A Benchmark Environment Motivated by Industrial Control Problemscited by 31

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Not cited yet.

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Close in wording, no citation either way

  1. 2020Hyperparameter Selection for Offline Reinforcement Learning
  2. 2021A Minimalist Approach to Offline Reinforcement Learning
  3. 2021OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation
  4. 2025Evaluation-Time Policy Switching for Offline Reinforcement Learning
  5. 2025Fully Offline Reinforcement Learning
  6. 2025Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis
  7. 2026Active Offline-to-Online Reinforcement Learning
  8. 2023Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidance