With the recent developments in robotic process automation (RPA) and artificial intelligence
(AI), academics and industrial practitioners are now pursuing robust and adaptive decision …

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 …

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 …

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 …

Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with
other agents through autonomous exploration of the environment. However, learning a …