  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interact to become outdated.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.
