Action-Sufficient State Representation Learning for Control with Structural Constraints

• URL: http://arxiv.org/abs/2110.05721v1
• Date: Tue, 12 Oct 2021 03:16:26 GMT
• Title: Action-Sufficient State Representation Learning for Control with Structural Constraints
• Authors: Biwei Huang, Chaochao Lu, Liu Leqi, Jos\'e Miguel Hern\'andez-Lobato, Clark Glymour, Bernhard Sch\"olkopf, Kun Zhang
• Abstract summary: In this paper, we focus on partially observable environments and propose to learn a minimal set of state representations that capture sufficient information for decision-making. We build a generative environment model for the structural relationships among variables in the system and present a principled way to characterize ASRs. Our empirical results on CarRacing and VizDoom demonstrate a clear advantage of learning and using ASRs for policy learning.
• Score: 21.47086290736692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks. In this paper, we focus on partially observable environments and propose to learn a minimal set of state representations that capture sufficient information for decision-making, termed \textit{Action-Sufficient state Representations} (ASRs). We build a generative environment model for the structural relationships among variables in the system and present a principled way to characterize ASRs based on structural constraints and the goal of maximizing cumulative reward in policy learning. We then develop a structured sequential Variational Auto-Encoder to estimate the environment model and extract ASRs. Our empirical results on CarRacing and VizDoom demonstrate a clear advantage of learning and using ASRs for policy learning. Moreover, the estimated environment model and ASRs allow learning behaviors from imagined outcomes in the compact latent space to improve sample efficiency.

Related papers

• Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning [63.58935783293342]
  Causal Bisimulation Modeling (CBM) is a method that learns the causal relationships in the dynamics and reward functions for each task to derive a minimal, task-specific abstraction. CBM's learned implicit dynamics models identify the underlying causal relationships and state abstractions more accurately than explicit ones.
  arXiv  Detail & Related papers  (2024-01-23T05:43:15Z)
• Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison [0.0]
  This study compares common auxiliary tasks based on, to the best of our knowledge, the only decoupled representation learning method for low-dimensional non-visual observations. Our findings show that representation learning with auxiliary tasks only provides performance gains in sufficiently complex environments.
  arXiv  Detail & Related papers  (2023-10-06T13:22:26Z)
• Learning World Models with Identifiable Factorization [39.767120163665574]
  We propose IFactor to model four distinct categories of latent state variables. Our analysis establishes block-wise identifiability of these latent variables. We present a practical approach to learning the world model with identifiable blocks.
  arXiv  Detail & Related papers  (2023-06-11T02:25:15Z)
• Hierarchical State Abstraction Based on Structural Information Principles [70.24495170921075]
  We propose a novel mathematical Structural Information principles-based State Abstraction framework, namely SISA, from the information-theoretic perspective. SISA is a general framework that can be flexibly integrated with different representation-learning objectives to improve their performances further.
  arXiv  Detail & Related papers  (2023-04-24T11:06:52Z)
• Latent Variable Representation for Reinforcement Learning [131.03944557979725]
  It remains unclear theoretically and empirically how latent variable models may facilitate learning, planning, and exploration to improve the sample efficiency of model-based reinforcement learning. We provide a representation view of the latent variable models for state-action value functions, which allows both tractable variational learning algorithm and effective implementation of the optimism/pessimism principle. In particular, we propose a computationally efficient planning algorithm with UCB exploration by incorporating kernel embeddings of latent variable models.
  arXiv  Detail & Related papers  (2022-12-17T00:26:31Z)
• Probing Statistical Representations For End-To-End ASR [28.833851817220616]
  This paper investigates cross-domain language model dependencies within transformer architectures using SVCCA. It was found that specific neural representations within the transformer layers exhibit correlated behaviour which impacts recognition performance.
  arXiv  Detail & Related papers  (2022-11-03T17:08:14Z)
• Information-Theoretic Odometry Learning [83.36195426897768]
  We propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation. The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language.
  arXiv  Detail & Related papers  (2022-03-11T02:37:35Z)
• Weakly Supervised Disentangled Representation for Goal-conditioned Reinforcement Learning [15.698612710580447]
  We propose a skill learning framework DR-GRL that aims to improve the sample efficiency and policy generalization. In a weakly supervised manner, we propose a Spatial Transform AutoEncoder (STAE) to learn an interpretable and controllable representation. We empirically demonstrate that DR-GRL significantly outperforms the previous methods in sample efficiency and policy generalization.
  arXiv  Detail & Related papers  (2022-02-28T09:05:14Z)
• Online reinforcement learning with sparse rewards through an active inference capsule [62.997667081978825]
  This paper introduces an active inference agent which minimizes the novel free energy of the expected future. Our model is capable of solving sparse-reward problems with a very high sample efficiency. We also introduce a novel method for approximating the prior model from the reward function, which simplifies the expression of complex objectives.
  arXiv  Detail & Related papers  (2021-06-04T10:03:36Z)
• Learning Robust State Abstractions for Hidden-Parameter Block MDPs [55.31018404591743]
  We leverage ideas of common structure from the HiP-MDP setting to enable robust state abstractions inspired by Block MDPs. We derive instantiations of this new framework for both multi-task reinforcement learning (MTRL) and meta-reinforcement learning (Meta-RL) settings.
  arXiv  Detail & Related papers  (2020-07-14T17:25:27Z)

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.