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Reinforcement Learning
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 …
Benjamin van Niekerk
,
Andreas Damianou
,
Benjamin Rosman
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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, …
Adam Earle
,
Andrew Saxe
,
Benjamin Rosman
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Project
Hierarchy Through Composition with Multitask LMDPs
Hierarchical architectures are critical to the scalability of reinforcement learning methods. Most current hierarchical frameworks …
Andrew Saxe
,
Adam Earle
,
Benjamin Rosman
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Project
Supplementary Material
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 …
Pablo Hernandez-Leal
,
Benjamin Rosman
,
Matthew Taylor
,
L Enrique Sucar
,
Enrique Munoz de Cote
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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 …
Benjamin Rosman
,
Majd Hawasly
,
Subramanian Ramamoorthy
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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 …
Pablo Hernandez-Leal
,
Matthew Taylor
,
Benjamin Rosman
,
L Enrique Sucar
,
Enrique Munoz de Cote
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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 …
Warwick Masson
,
Pravesh Ranchod
,
George Konidaris
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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 …
Pravesh Ranchod
,
Benjamin Rosman
,
George Konidaris
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Project
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 …
Benjamin Rosman
,
Bradley Hayes
,
Brian Scassellati
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Action Priors for Learning Domain Invariances
An agent tasked with solving a number of different decision making problems in similar environments has an opportunity to learn over a …
Benjamin Rosman
,
Subramanian Ramamoorthy
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