Reinforcement Learning

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 …
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Online Constrained Model-based Reinforcement Learning
Applying reinforcement learning to robotic systems poses a number of challenging problems. A key requirement is the ability to handle …
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Hierarchical Subtask Discovery with Non-negative Matrix Factorization
Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, …
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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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A Bayesian approach for Learning and Tracking Switching, Non-stationary Opponents
In many situations, agents are required to use a set of strategies (behaviors) and switch among them during the course of an …
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Bayesian Policy Reuse
A long-lived autonomous agent should be able to respond online to novel instances of tasks from a familiar domain. Acting online …
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Identifying and Tracking Switching, Non-stationary Opponents: a Bayesian Approach
In many situations, agents are required to use a set of strategies (behaviors) and switch among them during the course of an …
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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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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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