research
∙
08/15/2023
Planning to Learn: A Novel Algorithm for Active Learning during Model-Based Planning
Active Inference is a recent framework for modeling planning under uncer...
share
research
∙
05/31/2023
ROSARL: Reward-Only Safe Reinforcement Learning
An important problem in reinforcement learning is designing agents that ...
research
∙
03/28/2023
The challenge of redundancy on multi-agent value factorisation
In the field of cooperative multi-agent reinforcement learning (MARL), t...
research
∙
02/03/2023
Hierarchically Composing Level Generators for the Creation of Complex Structures
Procedural content generation (PCG) is a growing field, with numerous ap...
research
∙
11/01/2022
Reinforcement Learning in Education: A Multi-Armed Bandit Approach
Advances in reinforcement learning research have demonstrated the ways i...
research
∙
10/16/2022
A Framework for Undergraduate Data Collection Strategies for Student Support Recommendation Systems in Higher Education
Understanding which student support strategies mitigate dropout and impr...
research
∙
10/16/2022
Comparing Synthetic Tabular Data Generation Between a Probabilistic Model and a Deep Learning Model for Education Use Cases
The ability to generate synthetic data has a variety of use cases across...
research
∙
06/23/2022
World Value Functions: Knowledge Representation for Learning and Planning
We propose world value functions (WVFs), a type of goal-oriented general...
research
∙
05/25/2022
Skill Machines: Temporal Logic Composition in Reinforcement Learning
A major challenge in reinforcement learning is specifying tasks in a man...
research
∙
05/18/2022
World Value Functions: Knowledge Representation for Multitask Reinforcement Learning
An open problem in artificial intelligence is how to learn and represent...
research
∙
05/04/2022
Learning Abstract and Transferable Representations for Planning
We are concerned with the question of how an agent can acquire its own r...
research
∙
10/09/2021
Learning to Follow Language Instructions with Compositional Policies
We propose a framework that learns to execute natural language instructi...
research
∙
04/05/2021
FABRIC: A Framework for the Design and Evaluation of Collaborative Robots with Extended Human Adaptation
A limitation for collaborative robots (cobots) is their lack of ability ...
research
∙
02/02/2021
Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization
Training sparse networks to converge to the same performance as dense ne...
research
∙
04/07/2020
Online Constrained Model-based Reinforcement Learning
Applying reinforcement learning to robotic systems poses a number of cha...
research
∙
01/06/2020
A Boolean Task Algebra for Reinforcement Learning
We propose a framework for defining a Boolean algebra over the space of ...
research
∙
10/13/2019
If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on ReLU networks
Recent work in signal propagation theory has shown that dropout limits t...
research
∙
08/29/2019
Estimation of Body Mass Index from Photographs using Deep Convolutional Neural Networks
Obesity is an important concern in public health, and Body Mass Index is...
research
∙
05/28/2019
Learning Portable Representations for High-Level Planning
We present a framework for autonomously learning a portable representati...
research
∙
01/15/2019
Transfer Learning for Prosthetics Using Imitation Learning
In this paper, We Apply Reinforcement learning (RL) techniques to train ...
research
∙
07/12/2018
Will it Blend? Composing Value Functions in Reinforcement Learning
An important property for lifelong-learning agents is the ability to com...
research
∙
01/26/2018
Symbol Emergence in Cognitive Developmental Systems: a Survey
Symbol emergence through a robot's own interactive exploration of the wo...
research
∙
01/10/2018
Reasoning about Unforeseen Possibilities During Policy Learning
Methods for learning optimal policies in autonomous agents often assume ...
research
∙
08/01/2017
Hierarchical Subtask Discovery With Non-Negative Matrix Factorization
Hierarchical reinforcement learning methods offer a powerful means of pl...
research
∙
12/08/2016
Hierarchy through Composition with Linearly Solvable Markov Decision Processes
Hierarchical architectures are critical to the scalability of reinforcem...
research
∙
05/01/2015