A Study on the Role of Exploration in Multiagent Reinforcement Learning Considering Task Type
Abstract
Exploration-exploitation balance through temperature regulation in multiagent reinforcement learning of different task types is studied. Considered tasks are AND-type, OR-type, and their compositions. The presented study shows that, in contrary to AND-type tasks, the temperature should be set high at the beginning of learning of OR-type tasks and be reduced very gradually during the learning. It is also proposed that, the temperature control policy in learning composite tasks is decided based on the ratio of the number of redundant agents in the learning team to the team population. This ratio shows the similarity of composite task to the two main task types. Learned individual knowledge and the team performance in a simulated benchmarking task are employed for analysis of the presented methods.
Keywords
multiagent reinforcement learning, exploration-exploitation balance, temperature control, task type, redundant agent
