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 …
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ROSARL: Reward-Only Safe Reinforcement Learning
An important problem in reinforcement learning is designing agents that learn to solve tasks safely in an environment. A common …
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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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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 …
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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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Generalisable Agents for Neural Network Optimisation
Optimising deep neural networks is a challenging task due to complex training dynamics, high computational requirements, and long …
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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 …
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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 …
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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 …
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