12 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...
policies during the training procedure. This non-stationarity stems from breaking the Markov assumption that governs most Recent developments in deep reinforcement learning single-agent RL algorithms. Since the transitions and rewards are concerned with creating decision-making a...
Within the framework of Multi-Agent Reinforcement Learning, Social Learning is a new class of algorithms that enables agents to reshape the reward function of other agents with the goal of promoting cooperation and achieving higher global rewards in mixed-motive games. However, t...
A recent work [1] developed a shielding framework for single- Multi-agent reinforcement learning (MARL) has been increasingly agent reinforcement learning (RL), which synthesizes a shield to used in a wide range of safety-critical applications, which require enforce the correctne...
. The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Towards this goal, the Autonomous Agents Research Group develops novel machine learning algorithms fo...
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...
. Research on multi-agent interaction involving both mul- tiple artificial agents and humans is still in its infancy. Most recent ap- proaches have focused on environments with collaboration-focused human behavior, or providing only a small, defined set of situations. When deploy...
Many applications, e.g., in shared mobility, require coordinating a large number of agents. Mean-field reinforcement learning ad- dresses the resulting scalability challenge by optimizing the policy of a representative agent interacting with the infinite population of identical a...
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...
In this paper, we present the first MA shielding approach that is Deep reinforcement learning has emerged as a powerful tool for truly compositional, does not require online communication, and obtaining high-performance policies. However, the safety of these provides absolute saf...