Machine learning has been heavily researched and widely used in many disciplines.
However, achieving high accuracy requires a large amount of data that is sometimes …

To solve complex real-world problems with reinforcement learning, we cannot rely on
manually specified reward functions. Instead, we need humans to communicate an objective …

In shared autonomy, user input is combined with semi-autonomous control to achieve a
common goal. The goal is often unknown ex-ante, so prior work enables agents to infer the …

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

Training deep reinforcement learning agents complex behaviors in 3D virtual environments
requires significant computational resources. This is especially true in environments with …

We consider robot learning in the context of shared autonomy, where control of the system
can switch between a human teleoperator and autonomous control. In this setting we …

Value alignment is a property of intelligent agents wherein they solely pursue non-harmful
behaviors or human-beneficial goals. We introduce an approach to value-aligned …

In the recent past, algorithmic stock market trading for financial markets has undergone
significant growth and played a major role in investment decisions. Several methods have …

Interactive Reinforcement Learning (RL) enables agents to learn from two sources: rewards
taken from observations of the environment, and feedback or advice from a secondary critic …

Robots can use information from people to improve learning speed or quality. However,
people can have short attention spans and misunderstand tasks. Our work addresses these …