A review of safe reinforcement learning: Methods, theory and applications
Reinforcement learning (RL) has achieved tremendous success in many complex decision
making tasks. When it comes to deploying RL in the real world, safety concerns are usually …
making tasks. When it comes to deploying RL in the real world, safety concerns are usually …
Multi-agent reinforcement learning: Methods, applications, visionary prospects, and challenges
Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …
technique. However, current studies and applications need to address its scalability, non …
Safe multi-agent reinforcement learning for multi-robot control
A challenging problem in robotics is how to control multiple robots cooperatively and safely
in real-world applications. Yet, developing multi-robot control methods from the perspective …
in real-world applications. Yet, developing multi-robot control methods from the perspective …
Optimizing Long-Term Efficiency and Fairness in Ride-Hailing under Budget Constraint via Joint Order Dispatching and Driver Repositioning
Ride-hailing platforms (eg, Uber and Didi Chuxing) have become increasingly popular in
recent years. Efficiency has always been an important metric for such platforms. However …
recent years. Efficiency has always been an important metric for such platforms. However …
A faster decentralized algorithm for nonconvex minimax problems
In this paper, we study the nonconvex-strongly-concave minimax optimization problem on
decentralized setting. The minimax problems are attracting increasing attentions because of …
decentralized setting. The minimax problems are attracting increasing attentions because of …
Multi-agent constrained policy optimisation
Developing reinforcement learning algorithms that satisfy safety constraints is becoming
increasingly important in real-world applications. In multi-agent reinforcement learning …
increasingly important in real-world applications. In multi-agent reinforcement learning …
Taming communication and sample complexities in decentralized policy evaluation for cooperative multi-agent reinforcement learning
Cooperative multi-agent reinforcement learning (MARL) has received increasing attention in
recent years and has found many scientific and engineering applications. However, a key …
recent years and has found many scientific and engineering applications. However, a key …
Scalable primal-dual actor-critic method for safe multi-agent rl with general utilities
We investigate safe multi-agent reinforcement learning, where agents seek to collectively
maximize an aggregate sum of local objectives while satisfying their own safety constraints …
maximize an aggregate sum of local objectives while satisfying their own safety constraints …
Stability and generalization of the decentralized stochastic gradient descent ascent algorithm
The growing size of available data has attracted increasing interest in solving minimax
problems in a decentralized manner for various machine learning tasks. Previous theoretical …
problems in a decentralized manner for various machine learning tasks. Previous theoretical …
Learning safe control for multi-robot systems: Methods, verification, and open challenges
In this survey, we review the recent advances in control design methods for robotic multi-
agent systems (MAS), focusing on learning-based methods with safety considerations. We …
agent systems (MAS), focusing on learning-based methods with safety considerations. We …