Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms

• URL: http://arxiv.org/abs/2310.03225v1
• Date: Thu, 5 Oct 2023 00:47:09 GMT
• Title: Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms
• Authors: Akifumi Wachi, Wataru Hashimoto, Xun Shen, Kazumune Hashimoto
• Abstract summary: We present a solution of the safe exploration (GSE) problem in the form of a meta-algorithm for safe exploration, MASE. Our proposed algorithm achieves better performance than state-of-the-art algorithms on grid-world and Safety Gym benchmarks.
• Score: 8.789204441461678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safe exploration is essential for the practical use of reinforcement learning (RL) in many real-world scenarios. In this paper, we present a generalized safe exploration (GSE) problem as a unified formulation of common safe exploration problems. We then propose a solution of the GSE problem in the form of a meta-algorithm for safe exploration, MASE, which combines an unconstrained RL algorithm with an uncertainty quantifier to guarantee safety in the current episode while properly penalizing unsafe explorations before actual safety violation to discourage them in future episodes. The advantage of MASE is that we can optimize a policy while guaranteeing with a high probability that no safety constraint will be violated under proper assumptions. Specifically, we present two variants of MASE with different constructions of the uncertainty quantifier: one based on generalized linear models with theoretical guarantees of safety and near-optimality, and another that combines a Gaussian process to ensure safety with a deep RL algorithm to maximize the reward. Finally, we demonstrate that our proposed algorithm achieves better performance than state-of-the-art algorithms on grid-world and Safety Gym benchmarks without violating any safety constraints, even during training.

Related papers

• On Safety in Safe Bayesian Optimization [5.9045432488022485]
  We investigate three safety-related issues of the popular class of SafeOpt-type algorithms. First, these algorithms critically rely on frequentist bounds uncertainty for Gaussian Process (GP) regression. Second, we identify assuming an upper bound on the reproducing kernel Hilbert space (RKHS) norm of the target function. Third, SafeOpt and derived algorithms rely on a discrete search space, making them difficult to apply to higher-dimensional problems.
  arXiv  Detail & Related papers  (2024-03-19T17:50:32Z)
• SCPO: Safe Reinforcement Learning with Safety Critic Policy Optimization [1.3597551064547502]
  This study introduces a novel safe reinforcement learning algorithm, Safety Critic Policy Optimization. In this study, we define the safety critic, a mechanism that nullifies rewards obtained through violating safety constraints. Our theoretical analysis indicates that the proposed algorithm can automatically balance the trade-off between adhering to safety constraints and maximizing rewards.
  arXiv  Detail & Related papers  (2023-11-01T22:12:50Z)
• Approximate Model-Based Shielding for Safe Reinforcement Learning [83.55437924143615]
  We propose a principled look-ahead shielding algorithm for verifying the performance of learned RL policies. Our algorithm differs from other shielding approaches in that it does not require prior knowledge of the safety-relevant dynamics of the system. We demonstrate superior performance to other safety-aware approaches on a set of Atari games with state-dependent safety-labels.
  arXiv  Detail & Related papers  (2023-07-27T15:19:45Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)
• 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)
• SAUTE RL: Almost Surely Safe Reinforcement Learning Using State Augmentation [63.25418599322092]
  Satisfying safety constraints almost surely (or with probability one) can be critical for deployment of Reinforcement Learning (RL) in real-life applications. We address the problem by introducing Safety Augmented Markov Decision Processes (MDPs) We show that Saute MDP allows to view Safe augmentation problem from a different perspective enabling new features.
  arXiv  Detail & Related papers  (2022-02-14T08:57:01Z)
• Safe Policy Optimization with Local Generalized Linear Function Approximations [17.84511819022308]
  Existing safe exploration methods guaranteed safety under the assumption of regularity. We propose a novel algorithm, SPO-LF, that optimize an agent's policy while learning the relation between a locally available feature obtained by sensors and environmental reward/safety. We experimentally show that our algorithm is 1) more efficient in terms of sample complexity and computational cost and 2) more applicable to large-scale problems than previous safe RL methods with theoretical guarantees.
  arXiv  Detail & Related papers  (2021-11-09T00:47:50Z)
• Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations [64.39401322671803]
  This paper explores the possibility of safe RL algorithms with zero training-time safety violations. We propose an algorithm, Co-trained Barrier Certificate for Safe RL (CRABS), which iteratively learns barrier certificates, dynamics models, and policies.
  arXiv  Detail & Related papers  (2021-08-04T04:59:05Z)
• Safe Reinforcement Learning in Constrained Markov Decision Processes [20.175139766171277]
  We propose an algorithm, SNO-MDP, that explores and optimize Markov decision processes under unknown safety constraints. We provide theoretical guarantees on both the satisfaction of the safety constraint and the near-optimality of the cumulative reward.
  arXiv  Detail & Related papers  (2020-08-15T02:20:23Z)
• Provably Safe PAC-MDP Exploration Using Analogies [87.41775218021044]
  Key challenge in applying reinforcement learning to safety-critical domains is understanding how to balance exploration and safety. We propose Analogous Safe-state Exploration (ASE), an algorithm for provably safe exploration in MDPs with unknown, dynamics. Our method exploits analogies between state-action pairs to safely learn a near-optimal policy in a PAC-MDP sense.
  arXiv  Detail & Related papers  (2020-07-07T15:50:50Z)
• Verifiably Safe Exploration for End-to-End Reinforcement Learning [17.401496872603943]
  This paper contributes a first approach toward enforcing formal safety constraints on end-to-end policies with visual inputs. It is evaluated on a novel benchmark that emphasizes the challenge of safely exploring in the presence of hard constraints.
  arXiv  Detail & Related papers  (2020-07-02T16:12:20Z)

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.