Deep reinforcement learning has proved to be a fruitful method in various tasks in the field of
artificial intelligence during the last several years. Recent works have focused on deep …

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

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Game testing has been long recognized as a notoriously challenging task, which mainly
relies on manual playing and scripting based testing in game industry. Even until recently …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

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 …

A central capability of a long-lived reinforcement learning (RL) agent is to incrementally
adapt its behavior as its environment changes and to incrementally build upon previous …

When one agent interacts with a multi-agent environment, it is challenging to deal with
various opponents unseen before. Modeling the behaviors, goals, or beliefs of opponents …

In the course of playing or solving a game, it is common to face a series of changing other-
agent strategies. These strategies often share elements: the set of possible policies to play …