Safety Aware Reinforcement Learning (SARL)
As reinforcement learning agents become increasingly integrated into complex, real-world environments, designing for safety becomes a critical consideration. We specifically focus on researching scenarios where agents can cause undesired side effects while executing a policy on a primary task. Since one can define multiple tasks for a given environment dynamics, there are two important challenges. First, we need to abstract the concept of safety that applies broadly to that environment independent of the specific task being executed. Second, we need a mechanism for the abstracted notion of safety to modulate the actions of agents executing different policies to minimize their side-effects. In this work, we propose Safety Aware Reinforcement Learning (SARL) - a framework where a virtual safe agent modulates the actions of a main reward-based agent to minimize side effects. The safe agent learns a task-independent notion of safety for a given environment. The main agent is then trained with a regularization loss given by the distance between the native action probabilities of the two agents. Since the safe agent effectively abstracts a task-independent notion of safety via its action probabilities, it can be ported to modulate multiple policies solving different tasks within the given environment without further training. We contrast this with solutions that rely on task-specific regularization metrics and test our framework on the SafeLife Suite, based on Conway's Game of Life, comprising a number of complex tasks in dynamic environments. We show that our solution is able to match the performance of solutions that rely on task-specific side-effect penalties on both the primary and safety objectives while additionally providing the benefit of generalizability and portability.
Code (0)
등록된 구현이 없습니다.
Tasks
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Long-term Safe Reinforcement Learning with Binary Feedback
Safety is an indispensable requirement for applying reinforcement learning (RL) to real problems. Although there has been a surge of safe RL algorithms proposed in recent years, most existing work typically 1) relies on …
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Safe Reinforcement LearningSARL*: Deep Reinforcement Learning based Human-Aware Navigation for Mobile Robot in Indoor Environments
In a human-robot coexisting environment, reaching the goal position safely and efficiently is essential for a mobile service robot. In this paper, we present an advanced version of the Socially Attentive Reinforcement Le…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)SARL: Label-Free Reinforcement Learning by Rewarding Reasoning Topology
Reinforcement learning is critical to improving large reasoning models, but its success relies heavily on verifiable rewards (RLVR), making it hard to use in open-ended domains where correctness is ambiguous and cannot b…
Reinforcement LearningViSaRL: Visual Reinforcement Learning Guided by Human Saliency
Training robots to perform complex control tasks from high-dimensional pixel input using reinforcement learning (RL) is sample-inefficient, because image observations are comprised primarily of task-irrelevant informatio…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Robot ManipulationReducing Cognitive Overhead in Tool Use via Multi-Small-Agent Reinforcement Learning
Recent advances in multi-agent systems highlight the potential of specialized small agents that collaborate via division of labor. Existing tool-integrated reasoning systems, however, often follow a single-agent paradigm…
Reinforcement Learning