W Du, S Ding - Artificial Intelligence Review, 2021 - Springer
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 …
The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to produce RL algorithms whose policies generalise well to novel unseen situations at …
The advances in reinforcement learning have recorded sublime success in various domains. Although the multi-agent domain has been overshadowed by its single-agent counterpart …
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 …
Problems of cooperation--in which agents seek ways to jointly improve their welfare--are ubiquitous and important. They can be found at scales ranging from our daily routines--such …
Y Li - arXiv preprint arXiv:1701.07274, 2017 - arxiv.org
We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start …
From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has …
Abstract Theory of mind (ToM) broadly refers to humans' ability to represent the mental states of others, including their desires, beliefs, and intentions. We design a Theory of Mind …
SE Li - Reinforcement learning for sequential decision and …, 2023 - Springer
Similar to humans, RL agents use interactive learning to successfully obtain satisfactory decision strategies. However, in many cases, it is desirable to learn directly from …