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Found 5 papers, 1 papers with code
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals.
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.
Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL
In this work, we draw upon the idea of formal contracting from economics to overcome diverging incentives between agents in MARL.
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
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.
