52 citations · 100 across the 6 of their papers we have counts for
6 papers
Room Clearance with Feudal Hierarchical Reinforcement Learning
Henry Charlesworth, Adrian Millea, Eddie Pottrill +1
Reinforcement learning (RL) is a general framework that allows systems to learn autonomously through trial-and-error interaction with their environment. In recent years combining R…
Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning
Henry Charlesworth, Giovanni Montana
Training agents to autonomously learn how to use anthropomorphic robotic hands has the potential to lead to systems capable of performing a multitude of complex manipulation tasks…
PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals
Henry Charlesworth, Giovanni Montana
Learning with sparse rewards remains a significant challenge in reinforcement learning (RL), especially when the aim is to train a policy capable of achieving multiple different go…
Intrinsically motivated collective motion
Henry J. Charlesworth, Matthew S. Turner
Collective motion is found in various animal systems, active suspensions and robotic or virtual agents. This is often understood using high level models that directly encode select…
Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information
Henry Charlesworth
We introduce a new virtual environment for simulating a card game known as "Big 2". This is a four-player game of imperfect information with a relatively complicated action space (…
Stochastic entropy production arising from nonstationary thermal transport
Ian J. Ford, Zachary P. L. Laker, Henry J. Charlesworth
We compute statistical properties of the stochastic entropy production associated with the nonstationary transport of heat through a system coupled to a time dependent nonisotherma…