Learning from demonstration, or imitation learning, is the process of learning to act in an
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …

In this article, we study the problem of multiple stochastic agents interacting in a dynamic
game scenario with continuous state and action spaces. We define a new notion of …

Conclusions In this study, we presented the prospects for multi-agent system research with a
special focus on agent collaboration and gaming tasks. We briefly introduced some open …

This article proposes new inverse reinforcement learning (RL) algorithms to solve our
defined Adversarial Apprentice Games for nonlinear learner and expert systems. The games …

This paper focuses on Multi-Agent Reinforcement Learning (MARL) in non-cooperative
stochastic games, particularly addressing the challenge of task completion characterized by …

In this paper, we introduce two algorithms for estimating the cost function of expert players
engaged in optimal performance within linear continuous-time differential games. Initially …

Autonomous driving has emerged as one of the most active areas of research as it has the
promise of making transportation safer and more efficient than ever before. Most real-world …

Learning from imperfect demonstrations is a crucial challenge in imitation learning (IL).
Unlike existing works that still rely on the enormous effort of expert demonstrators, we …

This paper analyses a simple epsilon-greedy exploration approach to train models with
Deep Q-Learning algorithm to involve randomness that helps prevail the agent over …

Making decisions in the presence of a strategic opponent requires one to take into account
the opponent's ability to actively mask its intended objective. To describe such strategic …