Intelligent virtual agent, learning how to reach a goal by making the least number of compromises
Dilyana Budakova, Veselka Petrova-Dimitrova, Lyudmil Dakovski · IOP Conference Series Materials Science and Engineering · 2020
Abstract The learning process in the Q-learning algorithm is characterized by maximizing a single, numerical reward signal. However, there are tasks for which the requirements toward the way to reach a goal are complex. This paper proposes a modification to the Q-learning algorithm. In order to make the Q-learning agent find the optimal path to the goal by meeting particular complex criteria, the use of measures model (a model of environment criteria), represented as a new memory matrix, is introduced. If the goal cannot be reached by following the pre-set criteria, the learning agent can compromise a given criterion. The agent makes the least possible number of tradeoffs in order to reach the goal. If the criteria are arranged by their level of importance, then the agent can choose more in number and more acceptable compromises. The aim of the modification is to empower the learning agent to control the way of reaching a goal. The modified algorithm has been applied to training smart shopping-cart learning agents. The tests show improvement in their behaviour.