Transfer Learning

Optimal Task Generalisation in Cooperative Multi-Agent Reinforcement Learning
While task generalisation is widely studied in the context of single-agent reinforcement learning (RL), little research exists in the …
PDF Cite Project
Skill Machines: Temporal Logic Skill Composition in Reinforcement Learning
It is desirable for an agent to be able to solve a rich variety of problems that can be specified through language in the same …
PDF Cite Project
Transferable Dynamics Models for Efficient Object-Oriented Reinforcement Learning
The Reinforcement Learning (RL) framework offers a general paradigm for constructing autonomous agents that can make effective …
PDF Cite DOI
Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies
While reinforcement learning has achieved remarkable successes in several domains, its real-world application is limited due to many …
PDF Cite
World Value Functions: Knowledge Representation for Learning and Planning
We propose world value functions (WVFs), a type of goaloriented general value function that represents how to solve not just a given …
PDF Cite Project
Learning Abstract and Transferable Representations for Planning
We are concerned with the question of how an agent can acquire its own representations from sensory data. We restrict our focus to …
PDF Cite Project
World Value Functions: Knowledge Representation for Multitask Reinforcement Learning
An open problem in artificial intelligence is how to learn and represent knowledge that is sufficient for a general agent that needs to …
PDF Cite Project
Autonomous Learning of Object-Centric Abstractions for High-Level Planning
We propose a method for autonomously learning an object-centric representation of a continuous and high-dimensional environment that is …
PDF Cite Project
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
Investigating Transfer Learning in Graph Neural Networks
Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces. Transfer learning …
PDF Cite