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Found 28 papers, 9 papers with code
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms
We highlight that existing procedures that only use the expected return are limited on two fronts: first an infinite number of return distributions with a wide range of performance-reproducibility trade-offs can have the same expected return, limiting its effectiveness when used for comparing policies; second, the expected return metric does not leave any room for practitioners to choose the best trade-off value for considered applications.
Mix-ME: Quality-Diversity for Multi-Agent Learning
In many real-world systems, such as adaptive robotics, achieving a single, optimised solution may be insufficient.
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration
QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax.
Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning
This method uses a dynamics model, learned from interactions between the robot and the environment, to predict the robot's behaviour and improve sample efficiency.
Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains
Quality-Diversity (QD) algorithms are designed to generate collections of high-performing solutions while maximizing their diversity in a given descriptor space.
Enhancing MAP-Elites with Multiple Parallel Evolution Strategies
With the development of fast and massively parallel evaluations in many domains, Quality-Diversity (QD) algorithms, that already proved promising in a large range of applications, have seen their potential multiplied.
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning
However, we also find that not all insights from Deep RL can be effectively translated to QD-RL.
Efficient Exploration using Model-Based Quality-Diversity with Gradients
Methods such as Quality-Diversity deals with this by encouraging novel solutions and producing a diversity of behaviours.
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning
We present a Quality-Diversity benchmark suite for Deep Neuroevolution in Reinforcement Learning domains for robot control.
Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity
Quality-Diversity (QD) algorithms have been successfully used to make robots adapt to damages in seconds by leveraging a diverse set of learned skills.
Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors
Data-driven learning based methods have recently been particularly successful at learning robust locomotion controllers for a variety of unstructured terrains.
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery
Recent work has shown that training a mixture of policies, as opposed to a single one, that are driven to explore different regions of the state-action space can address this shortcoming by generating a diverse set of behaviors, referred to as skills, that can be collectively used to great effect in adaptation tasks or for hierarchical planning.
Learning to Walk Autonomously via Reset-Free Quality-Diversity
Quality-Diversity (QD) algorithms can discover large and complex behavioural repertoires consisting of both diverse and high-performing skills.
Accelerated Quality-Diversity through Massive Parallelism
With recent advances in simulators that run on accelerators, thousands of evaluations can now be performed in parallel on single GPU/TPU.
Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires
In this paper, we propose Dynamics-Aware Quality-Diversity (DA-QD), a framework to improve the sample efficiency of QD algorithms through the use of dynamics models.
Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention
The performance of a cross-sectional currency strategy depends crucially on accurately ranking instruments prior to portfolio construction.
Deep Learning for Market by Order Data
Market by order (MBO) data - a detailed feed of individual trade instructions for a given stock on an exchange - is arguably one of the most granular sources of microstructure information.
Building Cross-Sectional Systematic Strategies By Learning to Rank
The success of a cross-sectional systematic strategy depends critically on accurately ranking assets prior to portfolio construction.
Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering
In this paper, we present an approach to tactile pose estimation from the first touch for known objects.
Time Series Forecasting With Deep Learning: A Survey
Numerous deep learning architectures have been developed to accommodate the diversity of time series datasets across different domains.
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio
Detecting changes in asset co-movements is of much importance to financial practitioners, with numerous risk management benefits arising from the timely detection of breakdowns in historical correlations.
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i. e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically -- without any prior information on how they interact with the target.
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs
Despite recent innovations in network architectures and loss functions, training RNNs to learn long-term dependencies remains difficult due to challenges with gradient-based optimisation methods.
Enhancing Time Series Momentum Strategies Using Deep Neural Networks
While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule.
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction
Testing this on three real-world time series datasets, we demonstrate that the decoupled representations learnt not only improve the accuracy of one-step-ahead forecasts while providing realistic uncertainty estimates, but also facilitate multistep prediction through the separation of encoder stages.
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks
Electronic health records provide a rich source of data for machine learning methods to learn dynamic treatment responses over time.
Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning
Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time.
Disease-Atlas: Navigating Disease Trajectories with Deep Learning
Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time.
