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: This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning ...

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, ...