A Model of Predicting and Using Regularities by an Autonomous Agent

Abstract

AbstractIn this article, a computer model of an autonomous agent that functions in an environment with different levels of environmental illumination is developed and investigated. The level of illumination changes periodically. The task of the agent is to learn to eat in those moments of time when the illumination is radically increasing (at sunrise), and do not anything in the rest of the time. When the agent is feeding at sunrise, the agent receives essential positive reinforcement and its resource grows, otherwise the agent loses a small amount of resource. Two computer methods were used to train the agent: 1) the SARSA method, which is well known in the theory of reinforcement learning, and 2) the neural network method. Computer simulation demonstrated how the agent successfully learns and functions, accumulating a resource. It is shown that the results obtained by the SARSA method and the neural network method coincide. The model can be considered as a stage of investigation of more general properties of autonomous cognitive agents. KeywordsRegularity cognitionAutonomous agentPredicting regularities