Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer
This addresses challenges in multi-agent systems for dynamic environments, but appears incremental as it builds on existing reinforcement learning methods.
The paper tackles slow convergence and inefficient cooperation in cooperative multi-agent reinforcement learning by introducing a communication framework with a new state calculation method and a knowledge-transfer algorithm, resulting in accelerated learning and a reduced state space size.
Nowadays, cooperative multi-agent systems are used to learn how to achieve goals in large-scale dynamic environments. However, learning in these environments is challenging: from the effect of search space size on learning time to inefficient cooperation among agents. Moreover, reinforcement learning algorithms may suffer from a long time of convergence in such environments. In this paper, a communication framework is introduced. In the proposed communication framework, agents learn to cooperate effectively and also by introduction of a new state calculation method the size of state space will decline considerably. Furthermore, a knowledge-transferring algorithm is presented to share the gained experiences among the different agents, and develop an effective knowledge-fusing mechanism to fuse the knowledge learnt utilizing the agents' own experiences with the knowledge received from other team members. Finally, the simulation results are provided to indicate the efficacy of the proposed method in the complex learning task. We have evaluated our approach on the shepherding problem and the results show that the learning process accelerates by making use of the knowledge transferring mechanism and the size of state space has declined by generating similar states based on state abstraction concept.