Reinforcement Learning

Compositional Instruction Following with Language Models and Reinforcement Learning

Combining reinforcement learning with language grounding is challenging as the agent needs to explore the environment while …

Vanya Cohen, Geraud Nangue Tasse, Nakul Gopalan, Steven James, Matthew Gombolay, Ray Mooney, Benjamin Rosman

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

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 …

Geraud Nangue Tasse, Tamlin Love, Mark Nemecek, Steven James, Benjamin Rosman

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

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

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

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

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

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

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