Applying a Recurrent Neural Network to the Behaviour of an Autonomous Agent
Vanya Dimitrova Markova, Ventseslav Kirilov Shopov · 2023
This manuscript focuses on the application of deep recurrent learning techniques to build rational behaviour of an autonomous agent. The purpose of this study is to compare the encoder-decoder learning approach and classical Recurrent Neural Networks. The main hypothesis is that the encoder-decoder deep learning method is applicable in the area of sequential games and demonstrates better performance than classical Markov Deterministic Process learning. Thus, intelligent agents with a consistent approach can find better solutions to problems. in addition, in this manuscript, additional emphasis is placed on the training process in a dynamic sequential game environment.