mlsanityData to 14 Oct 2026

Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees

arXiv 2405.13290 ↗First posted 22 May 2024cs.LG cs.AIcited by 22

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

This research delves deeply into Meta Reinforcement Learning (Meta RL) through a exploration focusing on defining generalization limits and ensuring convergence. By employing a approach this article introduces an innovative theoretical framework to meticulously assess the effectiveness and performance of Meta RL algorithms. We present an explanation of generalization limits measuring how well these algorithms can adapt to learning tasks while maintaining consistent results. Our analysis delves into the factors that impact the adaptability of Meta RL revealing the relationship, between algorithm design and task complexity. Additionally we establish convergence assurances by proving conditions under which Meta RL strategies are guaranteed to converge towards solutions. We examine the convergence behaviors of Meta RL algorithms across scenarios providing a comprehensive understanding of the driving forces behind their long term performance. This exploration covers both convergence and real time efficiency offering a perspective, on the capabilities of these algorithms.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 14 references that are in the map. First posted on arXiv 22 May 2024.

Builds on

8 of 14 references.

References · 8

  1. 2018Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guaranteescited by 140
  2. 2024Large Language Models Can Learn Temporal Reasoningcited by 111
  3. 2024Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language Modelscited by 26
  4. 2024Adapting LLMs for Efficient Context Processing through Soft Prompt Compressioncited by 22
  5. 2024Localization Through Particle Filter Powered Neural Network Estimated Monocular Camera Posescited by 11
  6. 2024ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Modelscited by 10
  7. 2024Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniquescited by 8
  8. 2024Reinforcement Learning Approach for Integrating Compressed Contexts into Knowledge Graphscited by 5

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Citing papers per year: 2024: 13; 2025: 9.

22 papers.

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Similar, not cited

Close in wording, no citation either way

  1. 2024Meta-Reinforcement Learning with Universal Policy Adaptation: Provable Near-Optimality under All-task Optimum Comparator
  2. 2025How Should We Meta-Learn Reinforcement Learning Algorithms?
  3. 2024MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
  4. 2024A CMDP-within-online framework for Meta-Safe Reinforcement Learning
  5. 2024AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
  6. 2026Constrained Meta Reinforcement Learning with Provable Test-Time Safety
  7. 2021Offline Meta-Reinforcement Learning with Online Self-Supervision
  8. 2025An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning