Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees
- URL: http://arxiv.org/abs/2405.13290v1
- Date: Wed, 22 May 2024 02:09:22 GMT
- Title: Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees
- Authors: Cangqing Wang, Mingxiu Sui, Dan Sun, Zecheng Zhang, Yan Zhou,
- Abstract summary: This research delves deeply into Meta Reinforcement Learning (Meta RL) through a exploration focusing on defining generalization limits and ensuring convergence.
We present an explanation of generalization limits measuring how well these algorithms can adapt to learning tasks while maintaining consistent results.
- Score: 3.91366826418041
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: 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.
Related papers
- MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning [18.82398325614491]
We propose a new model-based approach to meta-RL, based on elements from existing state-of-the-art model-based and meta-RL methods.
We demonstrate the effectiveness of our approach on common meta-RL benchmark domains, attaining greater return with better sample efficiency.
In addition, we validate our approach on a slate of more challenging, higher-dimensional domains, taking a step towards real-world generalizing agents.
arXiv Detail & Related papers (2024-03-14T20:40:36Z) - Towards an Information Theoretic Framework of Context-Based Offline
Meta-Reinforcement Learning [50.976910714839065]
Context-based OMRL (COMRL) as a popular paradigm, aims to learn a universal policy conditioned on effective task representations.
We show that COMRL algorithms are essentially optimizing the same mutual information objective between the task variable $boldsymbolM$ and its latent representation $boldsymbolZ$ by implementing various approximate bounds.
Based on the theoretical insight and the information bottleneck principle, we arrive at a novel algorithm dubbed UNICORN, which exhibits remarkable generalization across a broad spectrum of RL benchmarks.
arXiv Detail & Related papers (2024-02-04T09:58:42Z) - Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and
Research Opportunities [63.258517066104446]
Reinforcement learning integrated as a component in the evolutionary algorithm has demonstrated superior performance in recent years.
We discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature.
In the applications of RL-EA section, we also demonstrate the excellent performance of RL-EA on several benchmarks and a range of public datasets.
arXiv Detail & Related papers (2023-08-25T15:06:05Z) - A Survey of Meta-Reinforcement Learning [83.95180398234238]
We cast the development of better RL algorithms as a machine learning problem itself in a process called meta-RL.
We discuss how, at a high level, meta-RL research can be clustered based on the presence of a task distribution and the learning budget available for each individual task.
We conclude by presenting the open problems on the path to making meta-RL part of the standard toolbox for a deep RL practitioner.
arXiv Detail & Related papers (2023-01-19T12:01:41Z) - On the Convergence Theory of Meta Reinforcement Learning with
Personalized Policies [26.225293232912716]
This paper proposes a novel personalized Meta-RL (pMeta-RL) algorithm.
It aggregates task-specific personalized policies to update a meta-policy used for all tasks, while maintaining personalized policies to maximize the average return of each task.
Experiment results show that the proposed algorithms outperform other previous Meta-RL algorithms on Gym and MuJoCo suites.
arXiv Detail & Related papers (2022-09-21T02:27:56Z) - Policy Mirror Descent for Regularized Reinforcement Learning: A
Generalized Framework with Linear Convergence [60.20076757208645]
This paper proposes a general policy mirror descent (GPMD) algorithm for solving regularized RL.
We demonstrate that our algorithm converges linearly over an entire range learning rates, in a dimension-free fashion, to the global solution.
arXiv Detail & Related papers (2021-05-24T02:21:34Z) - Improved Context-Based Offline Meta-RL with Attention and Contrastive
Learning [1.3106063755117399]
We improve upon one of the SOTA OMRL algorithms, FOCAL, by incorporating intra-task attention mechanism and inter-task contrastive learning objectives.
Theoretical analysis and experiments are presented to demonstrate the superior performance, efficiency and robustness of our end-to-end and model free method.
arXiv Detail & Related papers (2021-02-22T05:05:16Z) - FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance
Metric Learning and Behavior Regularization [10.243908145832394]
We study the offline meta-reinforcement learning (OMRL) problem, a paradigm which enables reinforcement learning (RL) algorithms to quickly adapt to unseen tasks.
This problem is still not fully understood, for which two major challenges need to be addressed.
We provide analysis and insight showing that some simple design choices can yield substantial improvements over recent approaches.
arXiv Detail & Related papers (2020-10-02T17:13:39Z) - A Brief Look at Generalization in Visual Meta-Reinforcement Learning [56.50123642237106]
We evaluate the generalization performance of meta-reinforcement learning algorithms.
We find that these algorithms can display strong overfitting when they are evaluated on challenging tasks.
arXiv Detail & Related papers (2020-06-12T15:17:17Z) - Discrete Action On-Policy Learning with Action-Value Critic [72.20609919995086]
Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension.
We construct a critic to estimate action-value functions, apply it on correlated actions, and combine these critic estimated action values to control the variance of gradient estimation.
These efforts result in a new discrete action on-policy RL algorithm that empirically outperforms related on-policy algorithms relying on variance control techniques.
arXiv Detail & Related papers (2020-02-10T04:23:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.