43 citations · 103 across the 59 of their papers we have counts for
13 papers · 1 filter
From Noise to Control: Parameterized Diffusion Policies
Renhao Zhang, Haotian Fu, Mingxi Jia +3
We propose Parameterized Diffusion Policy (PDP), a framework for learning diffusion policies conditioned on low-dimensional, continuous parameters embedded in a learned behavior ma…
Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
Martin Klissarov, Akhil Bagaria, Ziyan Luo +3
Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI). Hierarchical reinforcement le…
Automating Curriculum Learning for Reinforcement Learning using a Skill-Based Bayesian Network
Vincent Hsiao, Mark Roberts, Laura M. Hiatt +2
A major challenge for reinforcement learning is automatically generating curricula to reduce training time or improve performance in some target task. We introduce SEBNs (Skill-Env…
Latent-Predictive Empowerment: Measuring Empowerment without a Simulator
Andrew Levy, Alessandro Allievi, George Konidaris
Empowerment has the potential to help agents learn large skillsets, but is not yet a scalable solution for training general-purpose agents. Recent empowerment methods learn diverse…
MinePlanner: A Benchmark for Long-Horizon Planning in Large Minecraft Worlds
William Hill, Ireton Liu, Anita De Mello Koch +4
We propose a new benchmark for planning tasks based on the Minecraft game. Our benchmark contains 45 tasks overall, but also provides support for creating both propositional and nu…
Exploiting Contextual Structure to Generate Useful Auxiliary Tasks
Benedict Quartey, Ankit Shah, George Konidaris
Reinforcement learning requires interaction with an environment, which is expensive for robots. This constraint necessitates approaches that work with limited environmental interac…