Change: We will modify the reward for touching the ball and the existential reward/penalty to increase as the game progresses. This makes the agent more aggressive and strategic later in the episode.

Abstract: The rapid growth of machine learning (ML) technologies has raised significant concerns about their environmental impact, particularly regarding energy consumption and carbon emissions. This ...

This is a fork of the ML-Agents repository. The project extends the framework for studying the effects of sensory inputs and learning rates on soccer agents in a simulated environment. In the ...

Abstract: Data stream learning is an emerging machine learning paradigm designed for environments where data arrive continuously and must be processed in real time. Unlike traditional batch learning, ...