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PyCRM: A Python library for reward machine-based reinforcement learning
Reinforcement Learning (RL) research often models environments as Markov decision processes. Yet many real-world tasks are …
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An Unreasonably Simple Approach to Safe RL
An important problem in reinforcement learning is designing agents that learn to solve tasks safely in an environment. A common …
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Weighted Composition for Entropy-regularised Reinforcement Learning
One avenue for creating a generally intelligent agent is to equip it with the ability to combine its previously learned behaviours to …
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Compositionality and Systematicity Emerge from Iterated Learning in Deep Linear Networks
Humans have a remarkable ability to systematically generalize—reasoning about new situations by combining aspects of previous …
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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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Strengthening Robotics and Automation in Africa: Highlights of ICRA@40-Africa
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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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