A new multi-agent reinforcement learning algorithm and its application in wastewater reclamation by IBAC reactor

In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation to the behavior of an agent often depends on the other agents' behaviors. However, joint-action reinforcement learning suffers the slow convergence rate because of the enormous learning space produced by joint-action. In this article, a prediction-based reinforcement learning algorithm is presented for multi-agent cooperation tasks, which demands all agents to learn predicting the probabilities of actions that other agents may execute. An Immobilized Biological Activated Carbon (IBAC) reactor is run to test the efficacy of the new algorithm, and the result shows that the new algorithm can achieve high biodegradation efficiency much faster than the primitive reinforcement learning algorithm.