State-wise Constrained Policy Optimization

• URL: http://arxiv.org/abs/2306.12594v3
• Date: Mon, 17 Jun 2024 19:41:33 GMT
• Title: State-wise Constrained Policy Optimization
• Authors: Weiye Zhao, Rui Chen, Yifan Sun, Tianhao Wei, Changliu Liu,
• Abstract summary: State-wise Constrained Policy Optimization is the first general-purpose policy search algorithm for state-wise constrained reinforcement learning. We show that SCPO significantly outperforms existing methods and can handle state-wise constraints in high-dimensional robotics tasks.
• Score: 10.815583111876892
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement Learning (RL) algorithms have shown tremendous success in simulation environments, but their application to real-world problems faces significant challenges, with safety being a major concern. In particular, enforcing state-wise constraints is essential for many challenging tasks such as autonomous driving and robot manipulation. However, existing safe RL algorithms under the framework of Constrained Markov Decision Process (CMDP) do not consider state-wise constraints. To address this gap, we propose State-wise Constrained Policy Optimization (SCPO), the first general-purpose policy search algorithm for state-wise constrained reinforcement learning. SCPO provides guarantees for state-wise constraint satisfaction in expectation. In particular, we introduce the framework of Maximum Markov Decision Process, and prove that the worst-case safety violation is bounded under SCPO. We demonstrate the effectiveness of our approach on training neural network policies for extensive robot locomotion tasks, where the agent must satisfy a variety of state-wise safety constraints. Our results show that SCPO significantly outperforms existing methods and can handle state-wise constraints in high-dimensional robotics tasks.

Related papers

• Absolute State-wise Constrained Policy Optimization: High-Probability State-wise Constraints Satisfaction [20.00178731842195]
  Existing safe reinforcement learning (RL) methods only enforce state-wise constraints in expectation or enforce hard state-wise constraints with strong assumptions. We propose a novel general-purpose policy search algorithm that guarantees high-probability state-wise satisfaction for constraint systems. Our results show that ASCPO significantly outperforms existing methods in handling state-wise constraints across challenging continuous control tasks.
  arXiv  Detail & Related papers  (2024-10-02T03:43:33Z)
• Uniformly Safe RL with Objective Suppression for Multi-Constraint Safety-Critical Applications [73.58451824894568]
  The widely adopted CMDP model constrains the risks in expectation, which makes room for dangerous behaviors in long-tail states. In safety-critical domains, such behaviors could lead to disastrous outcomes. We propose Objective Suppression, a novel method that adaptively suppresses the task reward maximizing objectives according to a safety critic.
  arXiv  Detail & Related papers  (2024-02-23T23:22:06Z)
• 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)
• 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)
• State-wise Safe Reinforcement Learning: A Survey [5.826308050755618]
  State-wise constraints are one of the most common constraints in real-world applications. This paper provides a review of existing approaches that address state-wise constraints in RL.
  arXiv  Detail & Related papers  (2023-02-06T21:11:29Z)
• 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)
• Penalized Proximal Policy Optimization for Safe Reinforcement Learning [68.86485583981866]
  We propose Penalized Proximal Policy Optimization (P3O), which solves the cumbersome constrained policy iteration via a single minimization of an equivalent unconstrained problem. P3O utilizes a simple-yet-effective penalty function to eliminate cost constraints and removes the trust-region constraint by the clipped surrogate objective. We show that P3O outperforms state-of-the-art algorithms with respect to both reward improvement and constraint satisfaction on a set of constrained locomotive tasks.
  arXiv  Detail & Related papers  (2022-05-24T06:15:51Z)
• Constrained Policy Optimization via Bayesian World Models [79.0077602277004]
  LAMBDA is a model-based approach for policy optimization in safety critical tasks modeled via constrained Markov decision processes. We demonstrate LAMBDA's state of the art performance on the Safety-Gym benchmark suite in terms of sample efficiency and constraint violation.
  arXiv  Detail & Related papers  (2022-01-24T17:02:22Z)
• Constrained Model-Free Reinforcement Learning for Process Optimization [0.0]
  Reinforcement learning (RL) is a control approach that can handle nonlinear optimal control problems. Despite the promise exhibited, RL has yet to see marked translation to industrial practice. We propose an 'oracle'-assisted constrained Q-learning algorithm that guarantees the satisfaction of joint chance constraints with a high probability.
  arXiv  Detail & Related papers  (2020-11-16T13:16:22Z)
• Constrained Markov Decision Processes via Backward Value Functions [43.649330976089004]
  We model the problem of learning with constraints as a Constrained Markov Decision Process. A key contribution of our approach is to translate cumulative cost constraints into state-based constraints. We provide theoretical guarantees under which the agent converges while ensuring safety over the course of training.
  arXiv  Detail & Related papers  (2020-08-26T20:56:16Z)
• Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion [78.46388769788405]
  We introduce guided constrained policy optimization (GCPO), an RL framework based upon our implementation of constrained policy optimization (CPPO) We show that guided constrained RL offers faster convergence close to the desired optimum resulting in an optimal, yet physically feasible, robotic control behavior without the need for precise reward function tuning.
  arXiv  Detail & Related papers  (2020-02-22T10:15:53Z)

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.