Reinforcement learning (RL) methods usually treat reward functions as black boxes. As
such, these methods must extensively interact with the environment in order to discover …

Continuously learning new tasks using high-level ideas or knowledge is a key capability of
humans. In this paper, we propose lifelong reinforcement learning with sequential linear …

In this paper, we study the cooperative Multi-Agent Reinforcement Learning (MARL)
problems using Reward Machines (RMs) to specify the reward functions such that the prior …

As we progress towards real-world deployment, the critical need for interpretability in
reinforcement learning algorithms grows more pivotal, ensuring the safety and reliability of …

In order to be effective general purpose machines in real world environments, robots not
only will need to adapt their existing manipulation skills to new circumstances, they will need …

Reinforcement learning involves the study of how to solve sequential decision-making
problems using minimal supervision or prior knowledge. In contrast to most methods for …

Imitation learning learns how to act by observing the behavior of an expert demonstrator. We
are concerned with a setting where the demonstrations comprise only a subset of state …

End-to-end learning robotic manipulation with high data efficiency is one of the key
challenges in robotics. The latest methods that utilize human demonstration data and …

Reinforcement learning involves the study of how to solve sequential decision-making
problems using minimal supervision or prior knowledge. In contrast to most methods for …

In the field of intelligent manufacturing, manipulators are expected to have a higher level of
learning ability to master skills. In this paper, the method of visual support is adopted to …