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Reinforcement Learning
Improving Reinforcement Learning with Ensembles of Different Learners
Different reinforcement learning (RL) methods exist to address the problem of combining multiple different learners to generate a …
Gerrie Crafford
,
Benjamin Rosman
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Facilitating Safe Sim-to-Real through Simulator Abstraction and Zero-shot Task Composition
Simulators are a fundamental part of training robots to solve complex control and navigation tasks. This is due to the speed and safety …
Tamlin Love
,
Devon Jarvis
,
Geraud Nangue Tasse
,
Branden Ingram
,
Steven James
,
Benjamin Rosman
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Project
Video
Skill Machines: Temporal Logic Composition in Reinforcement Learning
A major challenge in reinforcement learning is specifying tasks in a manner that is both interpretable and verifiable. One common …
Geraud Nangue Tasse
,
Devon Jarvis
,
Steven James
,
Benjamin Rosman
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Project
Video
Reducing the Planning Horizon through Reinforcement Learning
Planning is a computationally expensive process, which can limit the reactivity of autonomous agents. Planning problems are usually …
Logan Dunbar
,
Benjamin Rosman
,
Anthony G. Cohn
,
Matteo Leonetti
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Who Should I Trust? Cautiously Learning with Unreliable Experts
An important problem in reinforcement learning is the need for greater sample efficiency. One approach to dealing with this problem is …
Tamlin Love
,
Ritesh Ajoodha
,
Benjamin Rosman
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Analyzing Reinforcement Learning Algorithms for Nitrogen Fertilizer Management in Simulated Crop Growth
Establishing intelligent crop management techniques for preserving the soil, while providing next-generational food supply for an …
Michael Vogt
,
Benjamin Rosman
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Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content
In this work, we consider the problem of procedural content generation for video game levels. Prior approaches have relied on …
Nicholas Muir
,
Steven James
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Project
World Value Functions: Knowledge Representation for Learning and Planning
We propose world value functions (WVFs), a type of goaloriented general value function that represents how to solve not just a given …
Geraud Nangue Tasse
,
Benjamin Rosman
,
Steven James
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Project
Accounting for the Sequential Nature of States to Learn Representations in Reinforcement Learning
In this work, we investigate the properties of data that cause popular representation learning approaches to fail. In particular, we …
Nathan Michlo
,
Devon Jarvis
,
Richard Klein
,
Steven James
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Adaptive Online Value Function Approximation with Wavelets
Using function approximation to represent a value function is necessary for continuous and high-dimensional state spaces. Linear …
Michael Beukman
,
Michael Mitcheley
,
Dean Wookey
,
Steven James
,
George Konidaris
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