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

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

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

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

Facilitating Safe Sim-to-Real through Simulator Abstraction and Zero-shot Task Composition

Simulators are a fundamental part of training robots to solve complex control and navigation tasks. This is due to the speed and safety …

Tamlin Love, Devon Jarvis, Geraud Nangue Tasse, Branden Ingram, Steven James, Benjamin Rosman

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