Multi-attribute Decision Making in a Complex Multiagent Environment Using Reinforcement Learning with Selective Perception

Abstract

Choosing between multiple alternative tasks is a hard problem for agents evolving in an uncertain real-time multiagent environment. An example of such environment is the RoboCupRescue simulation, where at each step an agent has to choose between a number of tasks. To do that, we have used a reinforcement learning technique where an agent learns the expected reward it should obtain if it chooses a particular task. Since all possible tasks can be described by a lot of attributes, we have used a selective perception technique to enable agents to narrow down the description of each task.