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An earth observation and explainable machine learning approach for determining the drivers of invasive species — a water hyacinth case study
Invasive species management is often constrained by limited resources and complicated by ecological and socio-economic variability …
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Composition and Zero-Shot Transfer with Lattice Structures in Reinforcement Learning
An important property of long-lived agents is the ability to reuse existing knowledge to solve new tasks. An appealing approach towards …
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Compositional Instruction Following with Language Models and Reinforcement Learning
Combining reinforcement learning with language grounding is challenging as the agent needs to explore the environment while …
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Transferable Dynamics Models for Efficient Object-Oriented Reinforcement Learning
The Reinforcement Learning (RL) framework offers a general paradigm for constructing autonomous agents that can make effective …
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MiDaS: A Large-Scale Minecraft Dataset for Non-Natural Image Benchmarking
Reinforcement learning (RL) has recently made several significant advances using video games as a testbed. While many of these games …
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Generating Interpretable Play-style Descriptions through Deep Unsupervised Clustering of Trajectories
In any game, play style is a concept that describes the technique and strategy employed by a player to achieve a goal. Identifying a …
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Hierarchically Composing Level Generators for the Creation of Complex Structures
Procedural content generation (PCG) is a growing field, with numerous applications in the video game industry and great potential to …
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Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations
We present a framework for the automatic encoding and repair of high-level tasks. Given a set of skills a robot can perform, our …
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FABRIC: A Framework for the Design and Evaluation of Collaborative Robots with Extended Human Adaptation
A limitation for collaborative robots (cobots) is their lack of ability to adapt to human partners, who typically exhibit an immense …
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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 …
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