Diversity is a growing research topic in Reinforcement Learning (RL). Previous research on
diversity has mainly focused on promoting diversity to encourage exploration and thereby …

Safety is critical to broadening the application of reinforcement learning (RL). Often, we train
RL agents in a controlled environment, such as a laboratory, before deploying them in the …

Safety is critical to broadening the application of reinforcement learning (RL). Often, RL
agents are trained in a controlled environment, such as a laboratory, before being deployed …

" Human-aware" has become a popular keyword used to describe a particular class of AI
systems that are designed to work and interact with humans. While there exists a surprising …

A common assumption in most Inverse Reinforcement Learning (IRL) methods is that human
demonstrations are drawn from an optimal policy. However, this assumption poses a …

Reinforcement Learning (RL) agents can solve general problems based on little to no
knowledge of the underlying environment. These agents learn through experience, using a …

In adversarial game environments, the objective agent aims to generate robust game
policies, ensuring high returns when facing different opponent policies consistently. Existing …

We propose to develop a theoretical and algorithmic foundation that will help create
autonomous robotic agents capable of executing patrol missions in urban environments …

在对抗博弈环境中, 目标智能体希望生成具有高鲁棒性的博弈策略, 使得目标智能体在面对不同
对手策略时, 始终具有较高的收益. 现有的基于自我博弈的策略生成方法通常会过拟合到针对 …