Single-Trajectory Distributionally Robust Reinforcement Learning

• URL: http://arxiv.org/abs/2301.11721v2
• Date: Sat, 21 Sep 2024 15:32:03 GMT
• Title: Single-Trajectory Distributionally Robust Reinforcement Learning
• Authors: Zhipeng Liang, Xiaoteng Ma, Jose Blanchet, Jiheng Zhang, Zhengyuan Zhou,
• Abstract summary: We propose Distributionally Robust RL (DRRL) to enhance performance across a range of environments. Existing DRRL algorithms are either model-based or fail to learn from a single sample trajectory. We design a first fully model-free DRRL algorithm, called distributionally robust Q-learning with single trajectory (DRQ)
• Score: 21.955807398493334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To mitigate the limitation that the classical reinforcement learning (RL) framework heavily relies on identical training and test environments, Distributionally Robust RL (DRRL) has been proposed to enhance performance across a range of environments, possibly including unknown test environments. As a price for robustness gain, DRRL involves optimizing over a set of distributions, which is inherently more challenging than optimizing over a fixed distribution in the non-robust case. Existing DRRL algorithms are either model-based or fail to learn from a single sample trajectory. In this paper, we design a first fully model-free DRRL algorithm, called distributionally robust Q-learning with single trajectory (DRQ). We delicately design a multi-timescale framework to fully utilize each incrementally arriving sample and directly learn the optimal distributionally robust policy without modelling the environment, thus the algorithm can be trained along a single trajectory in a model-free fashion. Despite the algorithm's complexity, we provide asymptotic convergence guarantees by generalizing classical stochastic approximation tools. Comprehensive experimental results demonstrate the superior robustness and sample complexity of our proposed algorithm, compared to non-robust methods and other robust RL algorithms.

Related papers

• Model-Free Robust Reinforcement Learning with Sample Complexity Analysis [16.477827600825428]
  This paper proposes a model-free DR-RL algorithm leveraging the Multi-level Monte Carlo technique. We develop algorithms for uncertainty sets defined by total variation, Chi-square divergence, and KL divergence. Remarkably, our algorithms represent the first model-free DR-RL approach featuring finite sample complexity.
  arXiv  Detail & Related papers  (2024-06-24T19:35:26Z)
• Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm [14.517103323409307]
  Sim-to-real gap represents disparity between training and testing environments. A promising approach to addressing this challenge is distributionally robust RL. We tackle robust RL via interactive data collection and present an algorithm with a provable sample complexity guarantee.
  arXiv  Detail & Related papers  (2024-04-04T16:40:22Z)
• Distributionally Robust Model-based Reinforcement Learning with Large State Spaces [55.14361269378122]
  Three major challenges in reinforcement learning are the complex dynamical systems with large state spaces, the costly data acquisition processes, and the deviation of real-world dynamics from the training environment deployment. We study distributionally robust Markov decision processes with continuous state spaces under the widely used Kullback-Leibler, chi-square, and total variation uncertainty sets. We propose a model-based approach that utilizes Gaussian Processes and the maximum variance reduction algorithm to efficiently learn multi-output nominal transition dynamics.
  arXiv  Detail & Related papers  (2023-09-05T13:42:11Z)
• Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior [51.60683890503293]
  We propose a theoretical framework for studying behavior cloning of complex expert demonstrations using generative modeling. We show that pure supervised cloning can generate trajectories matching the per-time step distribution of arbitrary expert trajectories.
  arXiv  Detail & Related papers  (2023-07-27T04:27:26Z)
• Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration [87.53543137162488]
  We propose an easy-to-implement online reinforcement learning (online RL) framework called textttMEX. textttMEX integrates estimation and planning components while balancing exploration exploitation automatically. It can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards.
  arXiv  Detail & Related papers  (2023-05-29T17:25:26Z)
• Stochastic Unrolled Federated Learning [85.6993263983062]
  We introduce UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning. Our proposed method tackles two challenges of this expansion, namely the need to feed whole datasets to the unrolleds and the decentralized nature of federated learning.
  arXiv  Detail & Related papers  (2023-05-24T17:26:22Z)
• Distributed Distributionally Robust Optimization with Non-Convex Objectives [24.64654924173679]
  Asynchronous distributed algorithm named Asynchronous Single-looP alternatIve gRadient projEction is proposed. New uncertainty set, i.e., constrained D-norm uncertainty set, is developed to leverage the prior distribution and flexibly control the degree of robustness. empirical studies on real-world datasets demonstrate that the proposed method can not only achieve fast convergence, but also remain robust against data as well as malicious attacks.
  arXiv  Detail & Related papers  (2022-10-14T07:39:13Z)
• Distributionally Robust Models with Parametric Likelihood Ratios [123.05074253513935]
  Three simple ideas allow us to train models with DRO using a broader class of parametric likelihood ratios. We find that models trained with the resulting parametric adversaries are consistently more robust to subpopulation shifts when compared to other DRO approaches.
  arXiv  Detail & Related papers  (2022-04-13T12:43:12Z)
• Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent [7.6860514640178]
  We propose a novel zeroth-order optimization algorithm for distributed reinforcement learning. It allows each agent to estimate its local gradient by cost evaluation independently, without use of any consensus protocol.
  arXiv  Detail & Related papers  (2021-07-26T18:11:07Z)
• Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization [85.84019017587477]
  Distributionally robust supervised learning is emerging as a key paradigm for building reliable machine learning systems for real-world applications. Existing algorithms for solving Wasserstein DRSL involve solving complex subproblems or fail to make use of gradients. We revisit Wasserstein DRSL through the lens of min-max optimization and derive scalable and efficiently implementable extra-gradient algorithms.
  arXiv  Detail & Related papers  (2021-04-27T16:56:09Z)

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.