Search Results for author: Steven James

Found 22 papers, 7 papers with code

Augmentative Topology Agents For Open-Ended Learning

1 code implementation20 Oct 2022 Muhammad Umair Nasir, Michael Beukman, Steven James, Christopher Wesley Cleghorn

In this work, we tackle the problem of open-ended learning by introducing a method that simultaneously evolves agents and increasingly challenging environments.

Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content

no code implementations29 Jul 2022 Nicholas Muir, Steven James

We propose a framework to tackle the procedural content generation problem that combines the best of ES and RL.

World Value Functions: Knowledge Representation for Learning and Planning

no code implementations23 Jun 2022 Geraud Nangue Tasse, Benjamin Rosman, Steven James

We propose world value functions (WVFs), a type of goal-oriented general value function that represents how to solve not just a given task, but any other goal-reaching task in an agent's environment.

Skill Machines: Temporal Logic Composition in Reinforcement Learning

no code implementations25 May 2022 Geraud Nangue Tasse, Devon Jarvis, Steven James, Benjamin Rosman

We introduce skill machines, a representation that can be learned directly from these reward machines that encode the solution to such tasks.

reinforcement-learning reinforcement Learning

World Value Functions: Knowledge Representation for Multitask Reinforcement Learning

no code implementations18 May 2022 Geraud Nangue Tasse, Steven James, Benjamin Rosman

In this work we propose world value functions (WVFs), which are a type of general value function with mastery of the world - they represent not only how to solve a given task, but also how to solve any other goal-reaching task.

reinforcement-learning reinforcement Learning

Learning Abstract and Transferable Representations for Planning

no code implementations4 May 2022 Steven James, Benjamin Rosman, George Konidaris

We propose a framework for autonomously learning state abstractions of an agent's environment, given a set of skills.

Adaptive Online Value Function Approximation with Wavelets

1 code implementation22 Apr 2022 Michael Beukman, Michael Mitchley, Dean Wookey, Steven James, George Konidaris

We further demonstrate that a fixed wavelet basis set performs comparably against the high-performing Fourier basis on Mountain Car and Acrobot, and that the adaptive methods provide a convenient approach to addressing an oversized initial basis set, while demonstrating performance comparable to, or greater than, the fixed wavelet basis.


Procedural Content Generation using Neuroevolution and Novelty Search for Diverse Video Game Levels

1 code implementation14 Apr 2022 Michael Beukman, Christopher W Cleghorn, Steven James

Procedurally generated video game content has the potential to drastically reduce the content creation budget of game developers and large studios.

Perceived Overlap: A Prerequisite for VAE Disentanglement

1 code implementation27 Feb 2022 Nathan Michlo, Steven James, Richard Klein

Learning disentangled representations with variational autoencoders (VAEs) is often attributed to the regularisation component of the loss.


Investigating Transfer Learning in Graph Neural Networks

no code implementations1 Feb 2022 Nishai Kooverjee, Steven James, Terence van Zyl

Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces.

Classification Graph Classification +2

Towards Objective Metrics for Procedurally Generated Video Game Levels

1 code implementation25 Jan 2022 Michael Beukman, Steven James, Christopher Cleghorn

With increasing interest in procedural content generation by academia and game developers alike, it is vital that different approaches can be compared fairly.

Learning to Follow Language Instructions with Compositional Policies

no code implementations9 Oct 2021 Vanya Cohen, Geraud Nangue Tasse, Nakul Gopalan, Steven James, Matthew Gombolay, Benjamin Rosman

We propose a framework that learns to execute natural language instructions in an environment consisting of goal-reaching tasks that share components of their task descriptions.

Generalisation in Lifelong Reinforcement Learning through Logical Composition

no code implementations ICLR 2022 Geraud Nangue Tasse, Steven James, Benjamin Rosman

We leverage logical composition in reinforcement learning to create a framework that enables an agent to autonomously determine whether a new task can be immediately solved using its existing abilities, or whether a task-specific skill should be learned.

reinforcement-learning reinforcement Learning +1

Autonomous Learning of Object-Centric Abstractions for High-Level Planning

no code implementations ICLR 2022 Steven James, Benjamin Rosman, George Konidaris

Such representations can immediately be transferred between tasks that share the same types of objects, resulting in agents that require fewer samples to learn a model of a new task.

Quantisation and Pruning for Neural Network Compression and Regularisation

1 code implementation14 Jan 2020 Kimessha Paupamah, Steven James, Richard Klein

Deep neural networks are typically too computationally expensive to run in real-time on consumer-grade hardware and low-powered devices.

 Ranked #1 on Network Pruning on CIFAR-10 (Inference Time (ms) metric)

Network Pruning Neural Network Compression

Inter- and Intra-domain Knowledge Transfer for Related Tasks in Deep Character Recognition

no code implementations2 Jan 2020 Nishai Kooverjee, Steven James, Terence van Zyl

In this paper we analyse the effectiveness of using deep transfer learning for character recognition tasks.

Transfer Learning

Learning Portable Representations for High-Level Planning

no code implementations ICML 2020 Steven James, Benjamin Rosman, George Konidaris

We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous environments.

Will it Blend? Composing Value Functions in Reinforcement Learning

no code implementations12 Jul 2018 Benjamin van Niekerk, Steven James, Adam Earle, Benjamin Rosman

An important property for lifelong-learning agents is the ability to combine existing skills to solve unseen tasks.

reinforcement-learning reinforcement Learning

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