  Exploration efficiency is a challenging problem in multi-agent reinforcement
learning (MARL), as the policy learned by confederate MARL depends on the
collaborative approach among multiple agents. Another important problem is the
less informative reward restricts the learning speed of MARL compared with the
informative label in supervised learning. In this work, we leverage on a novel
communication method to guide MARL to accelerate exploration and propose a
predictive network to forecast the reward of current state-action pair and use
the guidance learned by the predictive network to modify the reward function.
An improved prioritized experience replay is employed to better take advantage
of the different knowledge learned by different agents which utilizes
Time-difference (TD) error more effectively. Experimental results demonstrates
that the proposed algorithm outperforms existing methods in cooperative
multi-agent environments. We remark that this algorithm can be extended to
supervised learning to speed up its training.
