mlsanityData to 7 Oct 2026

Lipschitz Lifelong Reinforcement Learning

arXiv 2001.05411 ↗First posted 15 Jan 2020cs.LG cs.AI stat.MLcited by 23

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Abstract (arXiv)

We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks. We introduce a novel metric between Markov Decision Processes (MDPs) and establish that close MDPs have close optimal value functions. Formally, the optimal value functions are Lipschitz continuous with respect to the tasks space. These theoretical results lead us to a value-transfer method for Lifelong RL, which we use to build a PAC-MDP algorithm with improved convergence rate. Further, we show the method to experience no negative transfer with high probability. We illustrate the benefits of the method in Lifelong RL experiments.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jan 2020.

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Citing papers per year: 2021: 2; 2022: 5; 2023: 7; 2024: 4; 2025: 3; 2026: 2.

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  1. 2021Maximum Entropy RL (Provably) Solves Some Robust RL Problemscited by 169
  2. 2022Provable Benefits of Representational Transfer in Reinforcement Learningcited by 38
  3. 2023Resetting the Optimizer in Deep RL: An Empirical Studycited by 26
  4. 2022Investigating the Properties of Neural Network Representations in Reinforcement Learningcited by 24
  5. 2025A Survey of Continual Reinforcement Learningcited by 16
  6. 2022RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environmentscited by 13
  7. 2023Multi-agent Continual Coordination via Progressive Task Contextualizationcited by 10
  8. 2024A Survey Analyzing Generalization in Deep Reinforcement Learningcited by 8
  9. 2024Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theorycited by 7
  10. 2024Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidencecited by 5
  11. 2025Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learningcited by 5
  12. 2026MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitationcited by 4
11 more
  1. 2023OER: Offline Experience Replay for Continual Offline Reinforcement Learningcited by 3
  2. 2023Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learningcited by 3
  3. 2025The Policy Cliff: A Theoretical Analysis of Reward-Policy Maps in Large Language Modelscited by 3
  4. 2021Coarse-Grained Smoothness for RL in Metric Spacescited by 2
  5. 2022Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximationcited by 2
  6. 2022Robust Offline Reinforcement Learning with Gradient Penalty and Constraint Relaxationcited by 2
  7. 2023Human-Inspired Framework to Accelerate Reinforcement Learningcited by 1
  8. 2023Scaling Distributed Multi-task Reinforcement Learning with Experience Sharingcited by 1
  9. 2023Adversarial Online Multi-Task Reinforcement Learningcited by 0
  10. 2024CHIRPs: Change-Induced Regret Proxy metrics for Lifelong Reinforcement Learningcited by 0
  11. 2026Local Guidance, Global Impact: Gaussian-Reshaped Trust Region Unlocks Behavior Transitionscited by 0