Search Results for author:
Found 6 papers, 1 papers with code
SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control
To address these problems, we propose a new MARL method for traffic signal control, SocialLight, which learns cooperative traffic control policies by distributedly estimating the individual marginal contribution of agents on their local neighborhood.
Distributed Reinforcement Learning for Robot Teams: A Review
This review aims to provide an analysis of the state-of-the-art in distributed MARL for multi-robot cooperation.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent Systems
There, our comparison results show that FCMNet outperforms state-of-the-art communication-based reinforcement learning methods in all StarCraft II micromanagement tasks, and value decomposition methods in certain tasks.
DAN: Decentralized Attention-based Neural Network for the MinMax Multiple Traveling Salesman Problem
Encouraged by the recent developments in deep reinforcement learning (dRL), this work approaches the mTSP as a cooperative task and introduces DAN, a decentralized attention-based neural method that aims at tackling this key trade-off.
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World
However, the coordination of hundreds of agents in a real-life setting like a railway network remains challenging and the Flatland environment used for the competition models these real-world properties in a simplified manner.
Flatland-RL : Multi-Agent Reinforcement Learning on Trains
In order to probe the potential of Machine Learning (ML) research on Flatland, we (1) ran a first series of RL and IL experiments and (2) design and executed a public Benchmark at NeurIPS 2020 to engage a large community of researchers to work on this problem.
