A new accelerating algorithm for multi-agent reinforcement learning
张汝波, 仲宇, 顾国昌 · SIA OpenIR (Chinese Academy of Sciences) · 2005
In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation of the behavior of an agent often depends on the other agents' behaviors. However, joint -action reinforcement learning algorithms suffer the slow convergence rate because of the enormous learning space produced by jointaction. 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. A multi-robot cooperation experiment is run to test the efficacy of the new algorithm, and the experiment results show that the new algorithm can achieve the cooperation policy much faster than the primitive reinforcement learning algorithm.