The availability of challenging benchmarks has played a key role in the recent progress of
machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi …

Benchmarks play an important role in the development of machine learning algorithms. For
example, research in reinforcement learning (RL) has been heavily influenced by available …

Relative overgeneralization (RO) occurs in cooperative multi-agent learning tasks when
agents converge towards a suboptimal joint policy due to overfitting to suboptimal behaviors …

In human society, the conflict between self-interest and collective well-being often obstructs
efforts to achieve shared welfare. Related concepts like the Tragedy of the Commons and …

Decision-making problems, categorized as single-agent, eg, Atari, cooperative multi-agent,
eg, Hanabi, competitive multi-agent, eg, Hold'em poker, and mixed cooperative and …

Leveraging the experiences of other agents offers a powerful mechanism to enhance policy
optimization in multi-agent reinforcement learning (MARL). However, contemporary MARL …

Many real-world multiagent learning problems involve safety concerns. In these setups,
typical safe reinforcement learning algorithms constrain agents' behavior, limiting …

Existing multi-agent PPO algorithms lack compatibility with different types of parameter
sharing when extending the theoretical guarantee of PPO to cooperative multi-agent …

In this work, we study the problem of finding Pareto optimal policies in multi-agent
reinforcement learning problems with cooperative reward structures. We show that any …

Two-team zero-sum games are one of the most important paradigms in game theory. In this
paper, we focus on finding an unexploitable equilibrium in large team games. An …