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06/16/2023
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
Open-source reinforcement learning (RL) environments have played a cruci...
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03/31/2023
Reduce, Reuse, Recycle: Selective Reincarnation in Multi-Agent Reinforcement Learning
'Reincarnation' in reinforcement learning has been proposed as a formali...
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02/01/2023
Off-the-Grid MARL: a Framework for Dataset Generation with Baselines for Cooperative Offline Multi-Agent Reinforcement Learning
Being able to harness the power of large, static datasets for developing...
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09/21/2022
Towards a Standardised Performance Evaluation Protocol for Cooperative MARL
Multi-agent reinforcement learning (MARL) has emerged as a useful approa...
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06/14/2022
Universally Expressive Communication in Multi-Agent Reinforcement Learning
Allowing agents to share information through communication is crucial fo...
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11/12/2021
Causal Multi-Agent Reinforcement Learning: Review and Open Problems
This paper serves to introduce the reader to the field of multi-agent re...
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11/06/2021
On pseudo-absence generation and machine learning for locust breeding ground prediction in Africa
Desert locust outbreaks threaten the food security of a large part of Af...
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11/04/2021
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel
While multi-agent reinforcement learning has been used as an effective m...
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10/11/2021
Robust and Scalable SDE Learning: A Functional Perspective
Stochastic differential equations provide a rich class of flexible gener...
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07/03/2021
Mava: a research framework for distributed multi-agent reinforcement learning
Breakthrough advances in reinforcement learning (RL) research have led t...
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10/15/2020
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning
Multi-agent reinforcement learning has recently shown great promise as a...
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04/09/2020
On optimal transformer depth for low-resource language translation
Transformers have shown great promise as an approach to Neural Machine T...
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10/23/2019
Stabilising priors for robust Bayesian deep learning
Bayesian neural networks (BNNs) have developed into useful tools for pro...
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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...
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10/12/2019
On the expected behaviour of noise regularised deep neural networks as Gaussian processes
Recent work has established the equivalence between deep neural networks...
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04/16/2019
Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks
For our submission to the ZeroSpeech 2019 challenge, we apply discrete l...
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11/01/2018
Critical initialisation for deep signal propagation in noisy rectifier neural networks
Stochastic regularisation is an important weapon in the arsenal of a dee...
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06/14/2018
