Safety Constrained Multi|Agent Reinforcement Learning for Active ...

Safety Constrained Multi-Agent Reinforcement Learning for Active ...

Safety Constrained Multi-Agent Reinforcement Learning for. Active Voltage Control. Yang Qu, Jinming Ma, Feng Wu∗. School of Computer Science and Technology ...

Safety Constrained Multi-Agent Reinforcement Learning for Active ...

While Multi-Agent Reinforcement Learning (MARL) has emerged as a compelling approach to address this challenge, existing MARL approaches tend to ...

Safety Constrained Multi-Agent Reinforcement Learning for Active ...

In this paper, we formalize the active voltage control problem as a constrained Markov game and propose a safety-constrained MARL algorithm. We expand the ...

Safety-Constrained Multi-Agent Reinforcement Learning for Power ...

This article primarily explores how to mitigate the instability of renewable energy-based electricity generation through voltage control. Active ...

Safety Constrained Multi-Agent Reinforcement Learning for Active ...

Download Citation | Safety Constrained Multi-Agent Reinforcement Learning for Active Voltage Control | Active voltage control presents a ...

Multi-Agent Reinforcement Learning with Safety Layer for Active ...

However, these MARL algorithms do not explicitly guarantee that the power system satisfies the security constraints. There is a little in-depth ...

Safety Constrained Multi-Agent Reinforcement Learning for Active ...

Active voltage control presents a promising avenue for relieving power congestion and enhancing voltage quality, taking advantage of the ...

Safety-Constrained Multi-Agent Reinforcement Learning for Power ...

of voltage stabilizers to alleviate issues related to overvoltage and undervoltage [2]. Active power voltage control has always played a crucial ...

Multi-Agent Reinforcement Learning with Safety Layer for Active ...

In this paper, we formalize the active voltage control problem as a Constrained Markov Game and approach it with a centralized data-driven ...

Safety-Constrained Multi-Agent Reinforcement Learning for Power ...

Safety-Constrained Multi-Agent Reinforcement Learning for Power Quality Control in Distributed Renewable Energy Networks. Yongjiang Zhao, Haoyi Zhong, ...

Safe multi-agent reinforcement learning for multi-robot control

MACPO ensures both safety constraints satisfaction and monotonic performance improvement guarantee. •. Three safe MARL benchmarks are developed: Safe Multi- ...

Predictive Safety Network for Resource-constrained Multi-agent ...

solutions, optimization tools and other reinforcement learning methods. Keywords: Deep Reinforcement Learning, Multi-agent Systems, Motion and Task. Planning ...

Multi-Agent Reinforcement Learning : r/reinforcementlearning - Reddit

So far I have found RLib from Ray (27.1k stars, 4.7k forks) which seems to be active. MARLlib (509 stars, 79 forks). Acme (3.2k stars, 401 ...

Safe Multi-Agent Reinforcement Learning via Shielding - IFAAMAS

Safe reinforcement learning (RL) is an active research area, but existing results focus mostly on the single-agent setting [9], while safe MARL is still a ...

Reinforcement Learning without Safety Constraint Violations during ...

Engineering a reward signal that allows the agent to maximize its performance while remaining safe is not trivial. Safe RL studies how to mitigate such problems ...

Implicit Constraint Approach for Offline Multi-Agent Reinforcement ...

Recently, reinforcement learning (RL), an active learning process, has achieved massive success in various domains ranging from strategy games [59] to ...

Safe Reinforcement Learning with Natural Language Constraints

natural language constraints for safe RL. To this end, we first introduce HAZARD-. WORLD, a new multi-task benchmark that requires an agent to optimize reward.

Safe Multi-agent Reinforcement Learning with Protection Motivation ...

Safety constrained multi-agent reinforcement learning for active voltage control. In Proc. 33rd Int. Joint Conf. Artif. Intell., pp. 184–192, 2024. Ronald W ...

Multi-Agent Constrained Policy Optimisation (MACPO; MAPPO-L).

In this work, we formulate the safe multi-agent reinforcement learning problem as a constrained Markov game and solve it with trust region methods.

Stability Constrained Reinforcement Learning for Real-Time Voltage ...

Safety Constrained Multi-Agent Reinforcement Learning for Active Voltage Control · Engineering, Computer Science. Proceedings of the Thirty-ThirdInternational…