Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

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 …

Significant advances have recently been achieved in Multi-Agent Reinforcement Learning
(MARL) which tackles sequential decision-making problems involving multiple participants …

We present a novel procedure for optimization based on the combination of efficient
quantized tensor train representation and a generalized maximum matrix volume principle …

VDN and QMIX are two popular value-based algorithms for cooperative MARL that learn a
centralized action value function as a monotonic mixing of per-agent utilities. While this …

Value factorisation is a useful technique for multi-agent reinforcement learning (MARL) in
global reward game, however, its underlying mechanism is not yet fully understood. This …

Reinforcement Learning (RL) has achieved a plenty of breakthroughs in the past decade.
Notably, existing studies have shown that RL is suffered from poisoning attack, which results …

We consider model-based multi-agent reinforcement learning, where the environment
transition model is unknown and can only be learned via expensive interactions with the …

Value decomposition methods have gained popularity in the field of cooperative multi-agent
reinforcement learning. However, almost all existing methods follow the principle of …

Value factorization is a popular and promising approach to scaling up multi-agent
reinforcement learning in cooperative settings, which balances the learning scalability and …