Transfer Learning

Using NEAT to Learn Operators for Flexible Boolean Composition within Reinforcement Learning
Skill composition is a growing area of interest within Reinforcement Learning (RL) research. For example, if designing a robot for …
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Composition and Zero-Shot Transfer with Lattice Structures in Reinforcement Learning
An important property of long-lived agents is the ability to reuse existing knowledge to solve new tasks. An appealing approach towards …
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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 …
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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 …
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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 …
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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 …
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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 …
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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 …
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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 …
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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 …
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