Reinforcement Learning is an area of Machine Learning focused on how agents can be
trained to make sequential decisions, and achieve a particular goal within an arbitrary …

Abstract Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning
algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent …

Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous
disciplines, including game theory, economics, social sciences, and evolutionary biology …

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 …

The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL),
covering MARL's models, solution concepts, algorithmic ideas, technical challenges, and …

QMIX is a popular $ Q $-learning algorithm for cooperative MARL in the centralised training
and decentralised execution paradigm. In order to enable easy decentralisation, QMIX …

Role-based learning holds the promise of achieving scalable multi-agent learning by
decomposing complex tasks using roles. However, it is largely unclear how to efficiently …

Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve
complex cooperative tasks. Its success is partly because of parameter sharing among …

Abstract We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new
method for cooperative multi-agent reinforcement learning in both discrete and continuous …

Value decomposition recently injects vigorous vitality into multi-agent actor-critic methods.
However, existing decomposed actor-critic methods cannot guarantee the convergence of …