43 citations · 126 across the 29 of their papers we have counts for
12 papers · 1 filter
Augmentative Topology Agents For Open-Ended Learning
Muhammad Umair Nasir, Michael Beukman, Steven James +1
In this work, we tackle the problem of open-ended learning by introducing a method that simultaneously evolves agents and increasingly challenging environments. Unlike previous ope…
Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content
Nicholas Muir, Steven James
In this work, we consider the problem of procedural content generation for video game levels. Prior approaches have relied on evolutionary search (ES) methods capable of generating…
World Value Functions: Knowledge Representation for Learning and Planning
Geraud Nangue Tasse, Benjamin Rosman, Steven James
We propose world value functions (WVFs), a type of goal-oriented general value function that represents how to solve not just a given task, but any other goal-reaching task in an a…
World Value Functions: Knowledge Representation for Multitask Reinforcement Learning
Geraud Nangue Tasse, Steven James, Benjamin Rosman
An open problem in artificial intelligence is how to learn and represent knowledge that is sufficient for a general agent that needs to solve multiple tasks in a given world. In th…
Accounting for the Sequential Nature of States to Learn Features for Reinforcement Learning
Nathan Michlo, Devon Jarvis, Richard Klein +1
In this work, we investigate the properties of data that cause popular representation learning approaches to fail. In particular, we find that in environments where states do not s…
Learning Abstract and Transferable Representations for Planning
Steven James, Benjamin Rosman, George Konidaris
We are concerned with the question of how an agent can acquire its own representations from sensory data. We restrict our focus to learning representations for long-term planning,…