Learning Pursuer and Evasion Strategy Using the Q Network

Abstract

The game of pursuit-evasion has always been a popular research topic in the field of robotics Especially in the last decades when the agents turned into intelligent agents and they started to use the information about their environment for their own purposes without any initial information about the environment This tendency attracts remarkable amount of attention and opened the new area to newcomers from several different disciplines Reinforcement learning is a widely used method in pursuit-evasion domain when agents interact with the environment agents use the feedbacks (rewards and punishments) taken from the environment to learn and optimize their action With the help of reinforcement learning In this master’s thesis a research has done on multi-agent pursuit-evasion game problem in an environment with an obstacle by using reinforcement learning and the experimental results are submitted The intelligent agents use Deep Q-Learning algorithm and artificial potential field for the solution of the problem Two different approaches are accepted at the level of multi-robot cooperative systems to manage the interaction between agents These are team learning and concurrent learning In team learning agents are managed from a single center as a team and the mind used in learning is located in this center According to this idea although the agents are easy to manage the agents in the team do not have a say on their own so that in learning phase each pursuer will have knowledge of the other pursuers Their actions depend on the locations of the all pursuers evader and obstacles In concurrent learning each agent is an individual and is responsible for his own moves Each agent uses its own mind isolated from others to perform learning In concurrent learning pursuers action depends on their own location evader and obstacles In our work team learning and concurrent learning approaches are adapted for the learning of the pursuit team

Date of Award	2020
Original language	English
Supervisor	Chieh-Li Chen (Supervisor)