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Reinforcement Learning
Learning to Follow Language Instructions with Compositional Policies
We propose a framework that learns to execute natural language instructions in an environment consisting of goal-reaching tasks that …
Vanya Cohen
,
Geraud Nangue Tasse
,
Nakul Gopalan
,
Steven James
,
Matthew Gombolay
,
Benjamin Rosman
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Should I Trust You? Incorporating Unreliable Expert Advice in Human-Agent Interaction
A major concern in reinforcement learning, especially as it is applied to real-world and robotics problems, is that of …
Tamlin Love
,
Ritesh Ajoodha
,
Benjamin Rosman
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Deep Reinforcement Learning for Robotic Hand Manipulation
Researchers have made a lot of progress in combining the advances in Deep Learning and the generalization and applicability of …
Muhammed Saeed
,
Mohammed Nagdi
,
Benjamin Rosman
,
Hiba H.S.M. Ali
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Model Predictive-Actor Critic Reinforcement Learning for Dexterous Manipulation
Dexterous multi-fingered robotic hands represent a promising solution for robotic manipulators to perform a wide range of complex …
Muhammad Omer
,
Rami Ahmed
,
Benjamin Rosman
,
Sharief F. Babikir
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Knowledge Transfer using Model-Based Deep Reinforcement Learning
Deep reinforcement learning has recently been adopted for robot behavior learning, where robot skills are acquired and adapted from …
Tlou Boloka
,
Ndivhuwo Makondo
,
Benjamin Rosman
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A Boolean Task Algebra for Reinforcement Learning
The ability to compose learned skills to solve new tasks is an important property for lifelong-learning agents. In this work we …
Geraud Nangue Tasse
,
Steven James
,
Benjamin Rosman
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Logical Composition for Lifelong Reinforcement Learning
The ability to produce novel behaviours from existing skills is an important property of lifelong-learning agents. We build on recent …
Geraud Nangue Tasse
,
Steven James
,
Benjamin Rosman
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Learning Portable Representations for High-Level Planning
We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous …
Steven James
,
Benjamin Rosman
,
George Konidaris
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Learning Object-Centric Representations for High-Level Planning in Minecraft
We propose a method for autonomously learning an object-centric representation of a highdimensional environment that is suitable for …
Steven James
,
Benjamin Rosman
,
George Konidaris
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A Boolean Task Algebra for Reinforcement Learning
We propose a framework for defining a Boolean algebra over the space of tasks. This allows us to formulate new tasks in terms of the …
Geraud Nangue Tasse
,
Steven James
,
Benjamin Rosman
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Project
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