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Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

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...

Deep Reinforcement Learning for Multi-Agent Interaction

. 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...

An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative Tasks

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...

A Review of Causal Decision Making