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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 …
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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 …
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MinePlanner: A Benchmark for Long-Horizon Planning in Large Minecraft Worlds
We propose a new benchmark for planning tasks based on the Minecraft game. Our benchmark contains 45 tasks overall, but also provides …
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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 …
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Towards Financially Inclusive Credit Products Through Financial Time Series Clustering
Financial inclusion ensures that individuals have access to financial products and services that meet their needs. As a key …
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Generalisable Agents for Neural Network Optimisation
Optimising deep neural networks is a challenging task due to complex training dynamics, high computational requirements, and long …
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Hierarchical Reinforcement Learning with AI Planning Models
Deep Reinforcement Learning (DRL) has shown breakthroughs in solving challenging problems, such as pixel-based games and continuous …
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Preparing the Vuk'uzenzele and ZA-gov-multilingual South African Multilingual Corpora
This paper introduces two multilingual government themed corpora in various South African languages. The corpora were collected by …
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A Framework for Grassroots Research Collaboration in Machine Learning and Global Health
Traditional top-down approaches for global health have historically failed to achieve social progress (Hoffman et al., 2015; Hoffman …
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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 …
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