3 papers
We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal. This class of learning problems is difficult because of the often large combined action and observation spaces. In the fully centralized and decentralized approaches, we find...
Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learn- ing algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent settings. This is often due to the belief that PPO is significantly less sample efficient than off...
Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Com- Multi-Agent Reinforcement Learning (MARL) has recently emerged plex Fully Cooperative Tasks. In Proc. of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025), Detroit, Mich...