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

  • Piotr Papiez
  • Adrian HorzykEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)


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.


Neural networks Environment adapted reinforcement learning Knowledge-based learning and supervision Motivated learning Knowledge development Cognitive systems Cognitive robot 



This work was supported by AGH and the grant from the National Science Centre DEC-2016/21/B/ST7/02220.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakowPoland

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