Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning

• URL: http://arxiv.org/abs/2102.10774v1
• Date: Mon, 22 Feb 2021 05:05:16 GMT
• Title: Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning
• Authors: Lanqing Li, Yuanhao Huang, Dijun Luo
• Abstract summary: 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.
• Score: 1.3106063755117399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Meta-learning for offline reinforcement learning (OMRL) is an understudied problem with tremendous potential impact by enabling RL algorithms in many real-world applications. A popular solution to the problem is to infer task identity as augmented state using a context-based encoder, for which efficient learning of task representations remains an open challenge. In this work, we improve upon one of the SOTA OMRL algorithms, FOCAL, by incorporating intra-task attention mechanism and inter-task contrastive learning objectives for more effective task inference and learning of control. Theoretical analysis and experiments are presented to demonstrate the superior performance, efficiency and robustness of our end-to-end and model free method compared to prior algorithms across multiple meta-RL benchmarks.

Related papers

• Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks [53.44714413181162]
  This paper shows that when an agent is trained on a sufficiently diverse set of tasks, a generic policy-sharing algorithm with myopic exploration design can be sample-efficient. To the best of our knowledge, this is the first theoretical demonstration of the "exploration benefits" of MTRL.
  arXiv  Detail & Related papers  (2024-03-03T22:57:44Z)
• 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)
• M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation [0.7564784873669823]
  We propose Multimodal Contrastive Unsupervised Reinforcement Learning (M2CURL) Our approach employs a novel multimodal self-supervised learning technique that learns efficient representations and contributes to faster convergence of RL algorithms. We evaluate M2CURL on the Tactile Gym 2 simulator and we show that it significantly enhances the learning efficiency in different manipulation tasks.
  arXiv  Detail & Related papers  (2024-01-30T14:09:35Z)
• On Task-Relevant Loss Functions in Meta-Reinforcement Learning and Online LQR [9.355903533901023]
  We propose a sample-efficient meta-RL algorithm that learns a model of the system or environment at hand in a task-directed manner. As opposed to the standard model-based approaches to meta-RL, our method exploits the value information in order to rapidly capture the decision-critical part of the environment.
  arXiv  Detail & Related papers  (2023-12-09T04:52:28Z)
• A Survey of Meta-Reinforcement Learning [69.76165430793571]
  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)
• Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning [92.18524491615548]
  Contrastive self-supervised learning has been successfully integrated into the practice of (deep) reinforcement learning (RL) We study how RL can be empowered by contrastive learning in a class of Markov decision processes (MDPs) and Markov games (MGs) with low-rank transitions. Under the online setting, we propose novel upper confidence bound (UCB)-type algorithms that incorporate such a contrastive loss with online RL algorithms for MDPs or MGs.
  arXiv  Detail & Related papers  (2022-07-29T17:29:08Z)
• Meta Reinforcement Learning with Successor Feature Based Context [51.35452583759734]
  We propose a novel meta-RL approach that achieves competitive performance comparing to existing meta-RL algorithms. Our method does not only learn high-quality policies for multiple tasks simultaneously but also can quickly adapt to new tasks with a small amount of training.
  arXiv  Detail & Related papers  (2022-07-29T14:52:47Z)
• REIN-2: Giving Birth to Prepared Reinforcement Learning Agents Using Reinforcement Learning Agents [0.0]
  In this paper, we introduce a meta-learning scheme that shifts the objective of learning to solve a task into the objective of learning to learn to solve a task (or a set of tasks) Our model, named REIN-2, is a meta-learning scheme formulated within the RL framework, the goal of which is to develop a meta-RL agent that learns how to produce other RL agents. Compared to traditional state-of-the-art Deep RL algorithms, experimental results show remarkable performance of our model in popular OpenAI Gym environments.
  arXiv  Detail & Related papers  (2021-10-11T10:13:49Z)
• A Workflow for Offline Model-Free Robotic Reinforcement Learning [117.07743713715291]
  offline reinforcement learning (RL) enables learning control policies by utilizing only prior experience, without any online interaction. We develop a practical workflow for using offline RL analogous to the relatively well-understood for supervised learning problems. We demonstrate the efficacy of this workflow in producing effective policies without any online tuning.
  arXiv  Detail & Related papers  (2021-09-22T16:03:29Z)
• 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)
• Learning Context-aware Task Reasoning for Efficient Meta-reinforcement Learning [29.125234093368732]
  We propose a novel meta-RL strategy to achieve human-level efficiency in learning novel tasks. We decompose the meta-RL problem into three sub-tasks, task-exploration, task-inference and task-fulfillment. Our algorithm effectively performs exploration for task inference, improves sample efficiency during both training and testing, and mitigates the meta-overfitting problem.
  arXiv  Detail & Related papers  (2020-03-03T07:38:53Z)

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.