A two-layered multi-agent reinforcement learning model and algorithm |
| |
Authors: | Ben-Nian Wang Yang Gao Zhao-Qian Chen Jun-Yuan Xie Shi-Fu Chen |
| |
Affiliation: | aNational Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China;bDepartment of Computer Science and Technology, Tongling University, Tongling 244000, China |
| |
Abstract: | Multi-agent reinforcement learning technologies are mainly investigated from two perspectives of the concurrence and the game theory. The former chiefly applies to cooperative multi-agent systems, while the latter usually applies to coordinated multi-agent systems. However, there exist such problems as the credit assignment and the multiple Nash equilibriums for agents with them. In this paper, we propose a new multi-agent reinforcement learning model and algorithm LMRL from a layer perspective. LMRL model is composed of an off-line training layer that employs a single agent reinforcement learning technology to acquire stationary strategy knowledge and an online interaction layer that employs a multi-agent reinforcement learning technology and the strategy knowledge that can be revised dynamically to interact with the environment. An agent with LMRL can improve its generalization capability, adaptability and coordination ability. Experiments show that the performance of LMRL can be better than those of a single agent reinforcement learning and Nash-Q. |
| |
Keywords: | Reinforcement learning Multi-agent Layered model |
本文献已被 ScienceDirect 等数据库收录! |
|