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Reinforcement 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 …
Simon Rosen
,
Abdel Mfougouon Njupoun
,
Geraud Nangue Tasse
,
Steven James
,
Benjamin Rosman
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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 …
Geraud Nangue Tasse
,
Devon Jarvis
,
Steven James
,
Benjamin Rosman
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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 …
Ofir Marom
,
Benjamin Rosman
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DOI
Counting Reward Automata: Sample Efficient Reinforcement Learning Through the Exploitation of Reward Function Structure
We present counting reward automata—a finite state machine variant capable of modelling any reward function expressible as a …
Tristan Bester
,
Benjamin Rosman
,
Steven James
,
Geraud Nangue Tasse
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Project
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 …
Michael Beukman
,
Devon Jarvis
,
Richard Klein
,
Steven James
,
Benjamin Rosman
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Generalisable Agents for Neural Network Optimisation
Optimising deep neural networks is a challenging task due to complex training dynamics, high computational requirements, and long …
Kale-ab Tessera
,
Callum Tilbury
,
Sasha Abramowitz
,
Ruan de Kock
,
Omayma Mahjoub
,
Benjamin Rosman
,
Sara Hooker
,
Arnu Pretorius
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Hierarchical Reinforcement Learning with AI Planning Models
Deep Reinforcement Learning (DRL) has shown breakthroughs in solving challenging problems, such as pixel-based games and continuous …
Junkyu Lee
,
Michael Katz
,
Don Joven Agravante
,
Miao Liu
,
Geraud Nangue Tasse
,
Tim Klinger
,
Shirin Sohrabi
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The Challenge of Redundancy on Multi-agent Value Factorisation
In the field of cooperative multi-agent reinforcement learning (MARL), the standard paradigm is the use of centralised training and …
Siddarth Singh
,
Benjamin Rosman
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End-to-End Learning to Follow Language Instructions with Compositional Policies
We develop an end-to-end model for learning to follow language instructions with compositional policies. Our model combines large …
Vanya Cohen
,
Geraud Nangue Tasse
,
Nakul Gopalan
,
Steven James
,
Raymond Mooney
,
Benjamin Rosman
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Project
Skill Machines: Temporal Logic Composition in Reinforcement Learning
A major challenge in reinforcement learning is specifying tasks in a manner that is both interpretable and verifiable. One common …
Geraud Nangue Tasse
,
Devon Jarvis
,
Steven James
,
Benjamin Rosman
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