Motivated Reinforcement Learning Using Self-Developed Knowledge in Autonomous Cognitive Agent

This paper describes the development of a cognitive agent using motivated reinforcement learning. The conducted research was based on the example of a virtual robot, that placed in an unknown maze, was learned to reach a given goal optimally. The robot should expand knowledge about the surroundings and learn how to move in it to achieve a given target. The built-in motivation factors allow it to focus initially on collecting experiences instead of reaching the goal. In this way, the robot gradually broadens its knowledge with the advancement of exploration of its surroundings. The correctly formed knowledge is used for effective controlling the reinforcement learning routine to reach the target by the robot. In such a way, the motivation factors allow the robot to adapt and control its motivated reinforcement learning routine automatically and autonomously.