Deep reinforcement learning (DRL), leveraging Deep Learning (DL) in reinforcement
learning, has shown significant potential in achieving human-level autonomy in a wide …

Reinforcement learning is a crucial area of machine learning, with a wide range of
applications. To conduct experiments in this research field, it is necessary to define the …

We explore the generation of diverse environments using the Amorphous Fortress (AF)
simulation framework. AF defines a set of Finite State Machine (FSM) nodes and edges that …

Many aspects of humans' dynamic decision-making (DDM) behaviors have been studied
with computer-simulated games called microworlds. However, most microworlds only …

Imitation is a key component of human social behavior, and is widely used by both children
and adults as a way to navigate uncertain or unfamiliar situations. But in an environment …

The current state-of-the-art deep reinforcement learning techniques require agents to gather
large amounts of diverse experiences to train effective and general models. In addition …

ABSTRACT Dynamic Difficulty Adjustment studies how games can adapt content to their
users' skill level, aiming to keep them in flow. Most of these methods maximize engagement …