Reinforcement Learning

In this work, we consider the problem of procedural content generation for video game levels. Prior approaches have relied on …

We propose world value functions (WVFs), a type of goaloriented general value function that represents how to solve not just a given …

In this work, we investigate the properties of data that cause popular representation learning approaches to fail. In particular, we …

Using function approximation to represent a value function is necessary for continuous and high-dimensional state spaces. Linear …

We are concerned with the question of how an agent can acquire its own representations from sensory data. We restrict our focus to …

An open problem in artificial intelligence is how to learn and represent knowledge that is sufficient for a general agent that needs to …

We propose a method for autonomously learning an object-centric representation of a continuous and high-dimensional environment that is …

We leverage logical composition in reinforcement learning to create a framework that enables an agent to autonomously determine whether …

We leverage logical composition in reinforcement learning to create a framework that enables an agent to autonomously determine whether …

We propose a framework that learns to execute natural language instructions in an environment consisting of goal-reaching tasks that …