Year: 2025
Type: conference-paper
Abstract: Effective decision-making is a key challenge in artificial intelligence, with Reinforcement Learning (RL) emerging as one of the main approaches.However, RL often depends on complex reward functions, which are difficult to design.Intrinsic motivation, inspired by psychological concepts like curiosity, offers an alternative by generating agent-driven rewards to foster exploration.This paper introduces intrinsic motivation into the Elastic Decision Transformer (EDT) framework for Offline RL.By using an auxiliary intrinsic loss, we enhance representation learning without altering fixed reward signals.Experiments in locomotion tasks demonstrate improved performance, underscoring the potential of intrinsic motivation to advance RL in offline settings.
Institution: University of Siena
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409478798