APPLICATION OF A TRANSFORMER FOR ENCODING STATES IN REINFORCEMENT LEARNING
D. A. Kozlov
Samara National Research University, Samara, Russia
Keywords: reinforcement learning, transformer, SAC
Abstract
This article explores the application of the transformer architecture for encoding states in reinforcement learning algorithms. A new approach is presented, which integrates transformers with existing methods, such as Soft Actor-Critic (SAC), to enhance their performance and generalization ability. The results of experimental studies show that the proposed approach can improve learning in complex tasks of acquiring movement skills in a three-dimensional space.
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