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Hierarchy Through Composition with Multitask LMDPs
Hierarchical architectures are critical to the scalability of reinforcement learning methods. Most current hierarchical frameworks …
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An Analysis of Monte Carlo Tree Search
Monte Carlo Tree Search (MCTS) is a family of directed search algorithms that has gained widespread attention in recent years. Despite …
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Trajectory Learning from Human Demonstrations via Manifold Mapping
This work proposes a framework that enables arbitrary robots with unknown kinematics models to imitate human demonstrations to acquire …
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Reinforcement Learning with Parameterized Actions
We introduce a model-free algorithm for learning in Markov decision processes with parameterized actions—discrete actions with …
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Autonomous Prediction of Performance-based Standards for Heavy Vehicles
In this paper we use content-based features to perform automatic classification of music pieces into genres. We categorise these …
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Single-labelled Music Genre Classification Using Content-Based Features
In this paper we use content-based features to perform automatic classification of music pieces into genres. We categorise these …
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Knowledge Transfer for Learning Robot Models via Local Procrustes Analysis
Learning of robot kinematic and dynamic models from data has attracted much interest recently as an alternative to manually defined …
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Nonparametric Bayesian Reward Segmentation for Skill Discovery Using Inverse Reinforcement Learning
We present a method for segmenting a set of unstructured demonstration trajectories to discover reusable skills using inverse …
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Enhancing Agent Safety through Autonomous Environment Adaptation
Exploration and self-directed learning are valuable components of early childhood development. This often comes at an unacceptable …
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CHAMP: A Bespoke Integrated System for Mobile Manipulation
Mobile manipulation is a robotics paradigm with the potential to make major contributions to a number of important domain areas. …
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