arXiv research

Improved Q-learning for multi-agent reinforcement learning by weighting joint action values.

In many real-world settings, a team of agents must coordinate their behaviour while acting in a decentralised way. At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifte…

QR-MIX models joint state-action values as a distribution to handle randomness in MARL.

QMIX combines per-agent values to create decentralised policies.

We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently. However, VDN and QMIX are representative examples that use the idea of factorization of the joint action-value function into individual ones f…

A new method improves ridesharing efficiency using QMIX.

Centralised training with decentralised execution is an important setting for cooperative deep multi-agent reinforcement learning due to communication constraints during execution and computational tractability in training. In this paper, we analyse value-based methods that are known to have superior performance in com…

FACMAC combines deep policy gradients with factored critic for multi-agent reinforcement learning.

New method combines value function decomposition and policy gradients for cooperative multi-agent reinforcement learning.

Reinforcement learning encounters major challenges in multi-agent settings, such as scalability and non-stationarity. Recently, value function factorization learning emerges as a promising way to address these challenges in collaborative multi-agent systems. However, existing methods have been focusing on learning full…

CollaQ improves multi-agent performance in StarCraft by 40% with fewer samples.