BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning

• URL: http://arxiv.org/abs/2407.10967v1
• Date: Mon, 15 Jul 2024 17:59:23 GMT
• Title: BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
• Authors: Haohong Lin, Wenhao Ding, Jian Chen, Laixi Shi, Jiacheng Zhu, Bo Li, Ding Zhao,
• Abstract summary: offline model-based reinforcement learning (MBRL) enhances data efficiency by utilizing pre-collected datasets to learn models and policies. This paper first identifies the primary source of this mismatch comes from the underlying confounders present in offline data. We introduce textbfBilintextbfEar textbfCAUSal rtextbfEpresentation(BECAUSE), an algorithm to capture causal representation for both states.
• Score: 39.090104460303415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Offline model-based reinforcement learning (MBRL) enhances data efficiency by utilizing pre-collected datasets to learn models and policies, especially in scenarios where exploration is costly or infeasible. Nevertheless, its performance often suffers from the objective mismatch between model and policy learning, resulting in inferior performance despite accurate model predictions. This paper first identifies the primary source of this mismatch comes from the underlying confounders present in offline data for MBRL. Subsequently, we introduce \textbf{B}ilin\textbf{E}ar \textbf{CAUS}al r\textbf{E}presentation~(BECAUSE), an algorithm to capture causal representation for both states and actions to reduce the influence of the distribution shift, thus mitigating the objective mismatch problem. Comprehensive evaluations on 18 tasks that vary in data quality and environment context demonstrate the superior performance of BECAUSE over existing offline RL algorithms. We show the generalizability and robustness of BECAUSE under fewer samples or larger numbers of confounders. Additionally, we offer theoretical analysis of BECAUSE to prove its error bound and sample efficiency when integrating causal representation into offline MBRL.

Related papers

• DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization [44.291382840373]
  This paper addresses the challenge of out-of-distribution generalization in graph machine learning. Traditional graph learning algorithms falter in real-world scenarios where this assumption fails. A principal factor contributing to this suboptimal performance is the inherent simplicity bias of neural networks.
  arXiv  Detail & Related papers  (2024-08-08T12:08:55Z)
• SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets [32.496818080222646]
  We propose a new approach to model-based offline reinforcement learning. We provide a theoretical guarantee of model uncertainty and performance bound of SeMOPO. Experimental results show that our method substantially outperforms all baseline methods.
  arXiv  Detail & Related papers  (2024-06-13T15:16:38Z)
• Simple Ingredients for Offline Reinforcement Learning [86.1988266277766]
  offline reinforcement learning algorithms have proven effective on datasets highly connected to the target downstream task. We show that existing methods struggle with diverse data: their performance considerably deteriorates as data collected for related but different tasks is simply added to the offline buffer. We show that scale, more than algorithmic considerations, is the key factor influencing performance.
  arXiv  Detail & Related papers  (2024-03-19T18:57:53Z)
• A PAC-Bayesian Perspective on the Interpolating Information Criterion [54.548058449535155]
  We show how a PAC-Bayes bound is obtained for a general class of models, characterizing factors which influence performance in the interpolating regime. We quantify how the test error for overparameterized models achieving effectively zero training error depends on the quality of the implicit regularization imposed by e.g. the combination of model, parameter-initialization scheme.
  arXiv  Detail & Related papers  (2023-11-13T01:48:08Z)
• Linear Regression with Distributed Learning: A Generalization Error Perspective [0.0]
  We investigate the performance of distributed learning for large-scale linear regression. We focus on the generalization error, i.e., the performance on unseen data. Our results show that the generalization error of the distributed solution can be substantially higher than that of the centralized solution.
  arXiv  Detail & Related papers  (2021-01-22T08:43:28Z)
• Model-based Policy Optimization with Unsupervised Model Adaptation [37.09948645461043]
  We investigate how to bridge the gap between real and simulated data due to inaccurate model estimation for better policy optimization. We propose a novel model-based reinforcement learning framework AMPO, which introduces unsupervised model adaptation. Our approach achieves state-of-the-art performance in terms of sample efficiency on a range of continuous control benchmark tasks.
  arXiv  Detail & Related papers  (2020-10-19T14:19:42Z)
• Provably Efficient Causal Reinforcement Learning with Confounded Observational Data [135.64775986546505]
  We study how to incorporate the dataset (observational data) collected offline, which is often abundantly available in practice, to improve the sample efficiency in the online setting. We propose the deconfounded optimistic value iteration (DOVI) algorithm, which incorporates the confounded observational data in a provably efficient manner.
  arXiv  Detail & Related papers  (2020-06-22T14:49:33Z)
• How Training Data Impacts Performance in Learning-based Control [67.7875109298865]
  This paper derives an analytical relationship between the density of the training data and the control performance. We formulate a quality measure for the data set, which we refer to as $rho$-gap. We show how the $rho$-gap can be applied to a feedback linearizing control law.
  arXiv  Detail & Related papers  (2020-05-25T12:13:49Z)
• Decomposed Adversarial Learned Inference [118.27187231452852]
  We propose a novel approach, Decomposed Adversarial Learned Inference (DALI) DALI explicitly matches prior and conditional distributions in both data and code spaces. We validate the effectiveness of DALI on the MNIST, CIFAR-10, and CelebA datasets.
  arXiv  Detail & Related papers  (2020-04-21T20:00:35Z)

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.