Reinforcement Learning

Generalisation in Lifelong Reinforcement Learning through Logical Composition
We leverage logical composition in reinforcement learning to create a framework that enables an agent to autonomously determine whether …
PDF Cite Project
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 …
PDF Cite Project
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 …
PDF Cite
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 …
PDF Cite
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 …
PDF Cite
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 …
PDF Cite
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 …
PDF Cite Project
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 …
PDF Cite Project
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 …
PDF Cite Project
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 …
PDF Cite Project