9

Finding the FrameStack: Learning What to Remember for Non-Markovian Reinforcement Learning
Recent success in developing increasingly general purpose agents based on sequence models has led to increased focus on the problem of …
PDF Cite
Procedural Generation of Semantically Correct Levels in Video Games using Reward Shaping
The generation of video game levels traditionally relies on manual efforts from skilled professionals, resulting in significant …
PDF Cite Project
A Linear Network Theory of Iterated Learning
Language provides one of the primary examples of human’s ability to systematically generalize — reasoning about new …
PDF Cite
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 …
PDF Cite Project
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 …
PDF Cite
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 …
PDF Cite
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 …
PDF Cite Project
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 …
PDF Cite
Generalisable Agents for Neural Network Optimisation
Optimising deep neural networks is a challenging task due to complex training dynamics, high computational requirements, and long …
PDF Cite
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 …
PDF Cite