Scalable and Robust Multi|Agent Reinforcement Learning

Robust and Scalable Multiagent Reinforcement Learning in ...

... multi-player video games, and robot team sports. Key challenges of multiagent learning include the presence of uncertainty in the other agent's behaviors ...

Robust and Scalable Routing with Multi-Agent Deep Reinforcement ...

In this paper, we present DeepCQ+ routing which, in a novel manner, integrates emerging multi-agent deep reinforcement learning (MADRL) techniques into ...

A Scalable and Robust Multi-Agent Approach to Distributed ...

Multi- agent reinforcement learning for planning and scheduling multiple goals. In Proc. of the Fourth Intl Conference on. MultiAgent Systems. pages 359-360 ...

Scalable and Robust Multi-Agent Reinforcement Learning - Microsoft

Reinforcement Learning Day 2019: Scalable and Robust Multi-Agent Reinforcement Learning. S'ouvre dans un nouvel onglet. Date: October 3, 2019.

Scalable Multi-agent Reinforcement Learning for Factory-wide ...

Additionally, the proposed model provides the most robust scheduling performance to demand changes. Overall, the proposed MARL-based ...

Scalable and Robust Multi-Agent Reinforcement Learning - Microsoft

Scalable and Robust Multi-Agent Reinforcement Learning ; 日期：: 2019年10月3日 ; 演讲者：: Christopher Amato ; 所属机构：: Northeastern University ...

Scalable Multi-Agent Reinforcement Learning for Networked ...

It has long been recognized that multi-agent reinforcement learning (MARL) faces significant scalability issues due to the fact that the size of the state ...

Scalable Robust Multi-Agent Reinforcement Learning for Model ...

By analyzing nonlinear and time-varying aspects of a neural network via uncertainty models, a robust reinforcement learning procedure results that is guaranteed ...

Robust Multi-Agent Reinforcement Learning via Adversarial...

Multi-Agent Reinforcement Learning (MARL) has shown promising results across several domains. Despite this promise, MARL policies often lack ...

Scalable multi-agent reinforcement learning for distributed control of ...

Not relying on shared information may also improve the robustness of the solutions to failure of other agents, communication delays, and ...

Robust Multi-Agent Reinforcement Learning with Model Uncertainty

This work derives the policy gradients for robust MARL, and develops an actor-critic algorithm with function approximation, which outperforms several ...

MARLlib: A Scalable and Efficient Library For Multi-agent ...

E.1 Challenges of RLlib's Multi-Agent Case. While RLlib provides a robust infrastructure for reinforcement learning, the multi-agent case within RLlib poses ...

Deep Multi-agent Reinforcement Learning for Efficient and Scalable ...

These systems can connect with local communication networks, forming connected systems, and showing high scalability and robustness. Yet, controlling these ...

Scalable and Robust Multi-Agent Reinforcement Learning - Microsoft

Combining Machine Learning and Bayesian networks for Decision Support in Arrythmia Diagnosis March 20, 2024

Robust and Scalable Routing with Multi-Agent Deep Reinforcement ...

In this paper, we present a novel framework and routing policies, DeepCQ+ routing, using multi-agent deep reinforcement learning (MADRL) which ...

Scalable Communication for Multi-Agent Reinforcement Learning ...

When the number of agents varies,. TEM maintains superior performance without fur- ther training. 1 Introduction. Multi-agent reinforcement learning (MARL) has ...

Robust Multi-Agent Reinforcement Learning via Minimax Deep ...

In this paper, we focus on the problem of training robust DRL agents with continuous actions in the multi-agent learning setting so that the trained agents can ...

Scalable Reinforcement Learning Policies for Multi-Agent Control

We develop a Multi-Agent Reinforcement Learning (MARL) method to learn scalable control policies for target tracking. Our method can handle an arbitrary ...

Scalable Evaluation of Multi-Agent Reinforcement Learning with ...

Intriguingly, we find that maximizing collective reward often produces policies that are less robust to novel social situations than the policies obtained by ...

Robust Multi-Agent Reinforcement Learning with Model Uncertainty

In this work, we study the problem of multi-agent reinforcement learning (MARL) with model uncertainty, which is referred to as robust MARL.