Agent-based modeling (ABM) involves develo** models in which agents make adaptive
decisions in a changing environment. Machine-learning (ML) based inference models can …

Web testing has long been recognized as a notoriously difficult task. Even nowadays, web
testing still mainly relies on manual efforts in many cases while automated web testing is still …

This paper studies the vulnerability of deep reinforcement learning (DRL) models for power
systems topology optimization under data perturbations and cyber-attack. DRL has recently …

Despite achieving superior performance in human-level control problems, unlike humans,
deep reinforcement learning (DRL) lacks high-order intelligence (eg, logic deduction and …

Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample inefficiency.
Generally, episodic control-based approaches are solutions that leverage highly-rewarded …

Opponent modeling is essential to exploit sub-optimal opponents in strategic interactions.
Most previous works focus on building explicit models to predict the opponents' styles or …

Multi-Agent settings remain a fundamental challenge in the reinforcement learning (RL)
domain due to the partial observability and the lack of accurate real-time interactions across …

Bayesian policy reuse (BPR) is a general policy transfer framework for selecting a source
policy from an offline library by inferring the task belief based on some observation signals …

In Markov games, how to respond quickly and optimally for an agent against opponents that
follow changing policies is an open problem. Most state-of-the-art algorithms assume that …

Ad hoc teamwork is a crucial challenge that aims to design an agent capable of effective
collaboration with teammates employing diverse strategies without prior coordination …