Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Decentralised Multi-Agent Reinforcement Learning for Dynamic and
  Uncertain Environments   Multi-Agent Reinforcement Learning (MARL) is a widely used technique for
optimization in decentralised control problems. However, most applications of
MARL are in static environments, and are not suitable when agent behaviour and
environment conditions are dynamic and uncertain. Addressing uncertainty in
such environments remains a challenging problem for MARL-based systems. The
dynamic nature of the environment causes previous knowledge of how agents
interact to become outdated. Advanced knowledge of potential changes through
prediction significantly supports agents converging to near-optimal control
solutions. In this paper we propose P-MARL, a decentralised MARL algorithm
enhanced by a prediction mechanism that provides accurate information regarding
up-coming changes in the environment. This prediction is achieved by employing
an Artificial Neural Network combined with a Self-Organising Map that detects
and matches changes in the environment. The proposed algorithm is validated in
a realistic smart-grid scenario, and provides a 92% Pareto efficient solution
to an electric vehicle charging problem.

31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math