Home
Projects
Publications
People
Join the Lab
Contact
Login
Reinforcement Learning
Combining Primitive DQNs for Improved Reinforcement Learning in Minecraft
We ask whether a reinforcement learning agent learns better by first learning the skills to perform smaller tasks in a complex …
Matthew Reynard
,
Herman Kamper
,
Herman A Engelbrecht
,
Benjamin Rosman
PDF
Cite
Learning Options from Demonstration using Skill Segmentation
We present a method for learning options from segmented demonstration trajectories. The trajectories are first segmented into skills …
Matthew Cockcroft
,
Shahil Mawjee
,
Steven James
,
Pravesh Ranchod
PDF
Cite
Project
Understanding Structure of Concurrent Actions
Whereas most work in reinforcement learning (RL) ignores the structure or relationships between actions, in this paper we show that …
Perusha Moodley
,
Benjamin Rosman
,
Xia Hong
PDF
Cite
Composing Value Functions in Reinforcement Learning
An important property for lifelong-learning agents is the ability to combine existing skills to solve new unseen tasks. In general, …
Benjamin van Niekerk
,
Steven James
,
Adam Earle
,
Benjamin Rosman
PDF
Cite
Project
Supplementary Material
Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces
Parameterised actions in reinforcement learning are composed of discrete actions with continuous action-parameters. This provides a …
Craig Bester
,
Steven James
,
George Konidaris
PDF
Cite
Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning
Object-oriented representations in reinforcement learning have shown promise in transfer learning, with previous research introducing a …
Ofir Marom
,
Benjamin Rosman
PDF
Cite
Supplementary Material
Learning to Plan with Portable Symbols
We present a framework for autonomously learning a portable symbolic representation that describes a collection of low-level continuous …
Steven James
,
Benjamin Rosman
,
George Konidaris
PDF
Cite
Project
Will it Blend? Composing Value Functions in Reinforcement Learning
An important property for lifelong-learning agents is the ability to combine existing skills to solve unseen tasks. In general, …
Benjamin van Niekerk
,
Steven James
,
Adam Earle
,
Benjamin Rosman
PDF
Cite
Project
Hierarchical Subtask Discovery with Non-negative Matrix Factorization
Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, …
Adam Earle
,
Andrew Saxe
,
Benjamin Rosman
PDF
Cite
Project
Belief Reward Shaping in Reinforcement Learning
A key challenge in many reinforcement learning problems is delayed rewards, which can significantly slow down learning. Although reward …
Ofir Marom
,
Benjamin Rosman
PDF
Cite
Supplementary Material
«
»
Cite
×