Search Results for author:
Found 28 papers, 12 papers with code
MinePlanner: A Benchmark for Long-Horizon Planning in Large Minecraft Worlds
We propose a new benchmark for planning tasks based on the Minecraft game.
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 formal language.
Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies
To this end, we introduce a neural network architecture, the Decision Adapter, which generates the weights of an adapter module and conditions the behaviour of an agent on the context information.
LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization
By merging the code-generating abilities of LLMs with the diversity and robustness of QD solutions, we introduce \texttt{LLMatic}, a Neural Architecture Search (NAS) algorithm.
ROSARL: Reward-Only Safe Reinforcement Learning
A common solution is for a human expert to define either a penalty in the reward function or a cost to be minimised when reaching unsafe states.
Hierarchically Composing Level Generators for the Creation of Complex Structures
Procedural content generation (PCG) is a growing field, with numerous applications in the video game industry and great potential to help create better games at a fraction of the cost of manual creation.
Augmentative Topology Agents For Open-Ended Learning
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
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
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 Skill Composition in Reinforcement Learning
The agent can then flexibly compose them both logically and temporally to provably achieve temporal logic specifications in any regular language, such as regular fragments of linear temporal logic.
World Value Functions: Knowledge Representation for Multitask Reinforcement Learning
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.
Accounting for the Sequential Nature of States to Learn Features for Reinforcement Learning
In this work, we investigate the properties of data that cause popular representation learning approaches to fail.
Learning Abstract and Transferable Representations for Planning
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
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
Procedurally generated video game content has the potential to drastically reduce the content creation budget of game developers and large studios.
Overlooked Implications of the Reconstruction Loss for VAE Disentanglement
Our findings demonstrate the subjective nature of disentanglement and the importance of considering the interaction between the ground-truth factors, data and notably, the reconstruction loss, which is under-recognised in the literature.
Investigating Transfer Learning in Graph Neural Networks
Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces.
Towards Objective Metrics for Procedurally Generated Video Game Levels
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
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
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.
Autonomous Learning of Object-Centric Abstractions for High-Level Planning
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.
Logical Composition in Lifelong Reinforcement Learning
The ability to produce novel behaviours from existing skills is an important property of lifelong learning agents.
Learning Options from Demonstration using Skill Segmentation
We present a method for learning options from segmented demonstration trajectories.
Quantisation and Pruning for Neural Network Compression and Regularisation
Deep neural networks are typically too computationally expensive to run in real-time on consumer-grade hardware and low-powered devices.
Ranked #1 on
Neural Network Compression
on CIFAR-10
A Boolean Task Algebra for Reinforcement Learning
The ability to compose learned skills to solve new tasks is an important property of lifelong-learning agents.
Inter- and Intra-domain Knowledge Transfer for Related Tasks in Deep Character Recognition
In this paper we analyse the effectiveness of using deep transfer learning for character recognition tasks.
Learning Portable Representations for High-Level Planning
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
An important property for lifelong-learning agents is the ability to combine existing skills to solve unseen tasks.
