Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs

• URL: http://arxiv.org/abs/2008.00311v3
• Date: Mon, 1 Mar 2021 20:51:27 GMT
• Title: Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs
• Authors: Aria HasanzadeZonuzy, Archana Bura, Dileep Kalathil and Srinivas Shakkottai
• Abstract summary: We characterize the relationship between safety constraints and the number of samples needed to ensure a desired level of accuracy. Our main finding is that compared to the best known bounds of the unconstrained regime, the sample of constrained RL algorithms are increased by a factor that is logarithmic in the number of constraints.
• Score: 13.922754427601491
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many physical systems have underlying safety considerations that require that the policy employed ensures the satisfaction of a set of constraints. The analytical formulation usually takes the form of a Constrained Markov Decision Process (CMDP). We focus on the case where the CMDP is unknown, and RL algorithms obtain samples to discover the model and compute an optimal constrained policy. Our goal is to characterize the relationship between safety constraints and the number of samples needed to ensure a desired level of accuracy -- both objective maximization and constraint satisfaction -- in a PAC sense. We explore two classes of RL algorithms, namely, (i) a generative model based approach, wherein samples are taken initially to estimate a model, and (ii) an online approach, wherein the model is updated as samples are obtained. Our main finding is that compared to the best known bounds of the unconstrained regime, the sample complexity of constrained RL algorithms are increased by a factor that is logarithmic in the number of constraints, which suggests that the approach may be easily utilized in real systems.

Related papers

• Model-Free Active Exploration in Reinforcement Learning [53.786439742572995]
  We study the problem of exploration in Reinforcement Learning and present a novel model-free solution. Our strategy is able to identify efficient policies faster than state-of-the-art exploration approaches.
  arXiv  Detail & Related papers  (2024-06-30T19:00:49Z)
• 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)
• Probabilistic Reach-Avoid for Bayesian Neural Networks [71.67052234622781]
  We show that an optimal synthesis algorithm can provide more than a four-fold increase in the number of certifiable states. The algorithm is able to provide more than a three-fold increase in the average guaranteed reach-avoid probability.
  arXiv  Detail & Related papers  (2023-10-03T10:52:21Z)
• Provably Efficient UCB-type Algorithms For Learning Predictive State Representations [55.00359893021461]
  The sequential decision-making problem is statistically learnable if it admits a low-rank structure modeled by predictive state representations (PSRs) This paper proposes the first known UCB-type approach for PSRs, featuring a novel bonus term that upper bounds the total variation distance between the estimated and true models. In contrast to existing approaches for PSRs, our UCB-type algorithms enjoy computational tractability, last-iterate guaranteed near-optimal policy, and guaranteed model accuracy.
  arXiv  Detail & Related papers  (2023-07-01T18:35:21Z)
• 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)
• Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm [4.128216503196621]
  We propose an On-policy Model-based Safe Deep RL algorithm in which we learn the transition dynamics of the environment in an online manner. We show that our algorithm is more sample efficient and results in lower cumulative hazard violations as compared to constrained model-free approaches.
  arXiv  Detail & Related papers  (2022-10-14T06:53:02Z)
• Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning [72.97229770329214]
  We introduce a general approach for seeking high dimensional non-linear optimization problems in which maintaining safety during learning is crucial. Our approach called LBSGD is based on applying a logarithmic barrier approximation with a carefully chosen step size. We demonstrate the effectiveness of our approach on minimizing violation in policy tasks in safe reinforcement learning.
  arXiv  Detail & Related papers  (2022-07-21T11:14:47Z)
• Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-free RL [43.672794342894946]
  Reward-free reinforcement learning (RF-RL) relies on random action-taking to explore the unknown environment without any reward feedback information. It remains unclear how such safe exploration requirement would affect the corresponding sample complexity in order to achieve the desired optimality of the obtained policy in planning. We propose a unified Safe reWard-frEe ExploraTion (SWEET) framework, and develop algorithms coined Tabular-SWEET and Low-rank-SWEET, respectively.
  arXiv  Detail & Related papers  (2022-06-28T15:00:45Z)
• Sample Complexity of Robust Reinforcement Learning with a Generative Model [0.0]
  We propose a model-based reinforcement learning (RL) algorithm for learning an $epsilon$-optimal robust policy. We consider three different forms of uncertainty sets, characterized by the total variation distance, chi-square divergence, and KL divergence. In addition to the sample complexity results, we also present a formal analytical argument on the benefit of using robust policies.
  arXiv  Detail & Related papers  (2021-12-02T18:55:51Z)
• Safe Continuous Control with Constrained Model-Based Policy Optimization [0.0]
  We introduce a model-based safe exploration algorithm for constrained high-dimensional control. We also introduce a practical algorithm that accelerates policy search with model-generated data.
  arXiv  Detail & Related papers  (2021-04-14T15:20:55Z)
• Adaptive Sampling for Best Policy Identification in Markov Decision Processes [79.4957965474334]
  We investigate the problem of best-policy identification in discounted Markov Decision (MDPs) when the learner has access to a generative model. The advantages of state-of-the-art algorithms are discussed and illustrated.
  arXiv  Detail & Related papers  (2020-09-28T15:22:24Z)

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.