Search Results for author:
Found 25 papers, 12 papers with code
Mava: a research library for distributed multi-agent reinforcement learning in JAX
Our criteria for such software is that it should be simple enough to use to implement new ideas quickly, while at the same time be scalable and fast enough to test those ideas in a reasonable amount of time.
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms.
Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learning
However, offline MARL is still in its infancy and therefore lacks standardised benchmark datasets and baselines typically found in more mature subfields of reinforcement learning (RL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Towards a Standardised Performance Evaluation Protocol for Cooperative MARL
Combining these recommendations, with novel insights from our analysis, we propose a standardised performance evaluation protocol for cooperative MARL.
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 an approach to networked system control.
Learning Dynamics of Linear Denoising Autoencoders
Here we develop theory for how noise influences learning in DAEs.
Critical initialisation for deep signal propagation in noisy rectifier neural networks
Simulations and experiments on real-world data confirm that our proposed initialisation is able to stably propagate signals in deep networks, while using an initialisation disregarding noise fails to do so.
Reduce, Reuse, Recycle: Selective Reincarnation in Multi-Agent Reinforcement Learning
'Reincarnation' in reinforcement learning has been proposed as a formalisation of reusing prior computation from past experiments when training an agent in an environment.
Universally Expressive Communication in Multi-Agent Reinforcement Learning
Allowing agents to share information through communication is crucial for solving complex tasks in multi-agent reinforcement learning.
On pseudo-absence generation and machine learning for locust breeding ground prediction in Africa
In this paper, we compare this random sampling approach to more advanced pseudo-absence generation methods, such as environmental profiling and optimal background extent limitation, specifically for predicting desert locust breeding grounds in Africa.
Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks
For our submission to the ZeroSpeech 2019 challenge, we apply discrete latent-variable neural networks to unlabelled speech and use the discovered units for speech synthesis.
If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on ReLU networks
Our results therefore suggest that, in the shallow-to-moderate depth setting, critical initialisation provides zero performance gains when compared to off-critical initialisations and that searching for off-critical initialisations that might improve training speed or generalisation, is likely to be a fruitless endeavour.
On the expected behaviour of noise regularised deep neural networks as Gaussian processes
Recent work has established the equivalence between deep neural networks and Gaussian processes (GPs), resulting in so-called neural network Gaussian processes (NNGPs).
Stabilising priors for robust Bayesian deep learning
Bayesian neural networks (BNNs) have developed into useful tools for probabilistic modelling due to recent advances in variational inference enabling large scale BNNs.
On Optimal Transformer Depth for Low-Resource Language Translation
Therefore, by showing that transformer models perform well (and often best) at low-to-moderate depth, we hope to convince fellow researchers to devote less computational resources, as well as time, to exploring overly large models during the development of these systems.
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning
Furthermore, the core utility of our imagination is deeply coupled with communication.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Robust and Scalable SDE Learning: A Functional Perspective
Stochastic differential equations provide a rich class of flexible generative models, capable of describing a wide range of spatio-temporal processes.
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel
Multi-agent reinforcement learning has been used as an effective means to study emergent communication between agents, yet little focus has been given to continuous acoustic communication.
Causal Multi-Agent Reinforcement Learning: Review and Open Problems
This paper serves to introduce the reader to the field of multi-agent reinforcement learning (MARL) and its intersection with methods from the study of causality.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs
In this context, multi-agent debate (MAD) has emerged as a promising strategy for enhancing the truthfulness of LLMs.
Generalisable Agents for Neural Network Optimisation
Optimising deep neural networks is a challenging task due to complex training dynamics, high computational requirements, and long training times.
On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL
Cooperative multi-agent reinforcement learning (MARL) has made substantial strides in addressing the distributed decision-making challenges.
Efficiently Quantifying Individual Agent Importance in Cooperative MARL
Measuring the contribution of individual agents is challenging in cooperative multi-agent reinforcement learning (MARL).
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning
Establishing sound experimental standards and rigour is important in any growing field of research.
A Geospatial Approach to Predicting Desert Locust Breeding Grounds in Africa
Desert locust swarms present a major threat to agriculture and food security.
