Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for
  Cooperative Multi-Agent Reinforcement Learning   In the real world, many tasks require multiple agents to cooperate with each
other under the condition of local observations. To solve such problems, many
multi-agent reinforcement learning methods based on Centralized Training with
Decentralized Execution have been proposed. One representative class of work is
value decomposition, which decomposes the global joint Q-value $Q_\text{jt}$
into individual Q-values $Q_a$ to guide individuals' behaviors, e.g. VDN
(Value-Decomposition Networks) and QMIX. However, these baselines often ignore
the randomness in the situation. We propose MMD-MIX, a method that combines
distributional reinforcement learning and value decomposition to alleviate the
above weaknesses. Besides, to improve data sampling efficiency, we were
inspired by REM (Random Ensemble Mixture) which is a robust RL algorithm to
explicitly introduce randomness into the MMD-MIX. The experiments demonstrate
that MMD-MIX outperforms prior baselines in the StarCraft Multi-Agent Challenge
(SMAC) environment.

16
The categories of this paper are: stat
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph