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Found 49 papers, 12 papers with code
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning
To enhance the interpretability of Reinforcement Learning (RL), we propose Revealing Evolutionary Action Consequence Trajectories (REACT).
A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis
With recent advancements in quantum computing technology, optimizing quantum circuits and ensuring reliable quantum state preparation have become increasingly vital.
Quantum Denoising Diffusion Models
In recent years, machine learning models like DALL-E, Craiyon, and Stable Diffusion have gained significant attention for their ability to generate high-resolution images from concise descriptions.
Quantum Advantage Actor-Critic for Reinforcement Learning
In this work, we propose a novel quantum reinforcement learning approach that combines the Advantage Actor-Critic algorithm with variational quantum circuits by substituting parts of the classical components.
Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms
Recent advances in Multi-Agent Reinforcement Learning have prompted the modeling of intricate interactions between agents in simulated environments.
ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering
In the realm of Multi-Agent Reinforcement Learning (MARL), prevailing approaches exhibit shortcomings in aligning with human learning, robustness, and scalability.
Challenges for Reinforcement Learning in Quantum Circuit Design
Quantum computing (QC) in the current NISQ era is still limited in size and precision.
Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements
Quantum computing, with its potential to enhance various machine learning tasks, allows significant advancements in kernel calculation and model precision.
Improving Parameter Training for VQEs by Sequential Hamiltonian Assembly
A central challenge in quantum machine learning is the design and training of parameterized quantum circuits (PQCs).
Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines
Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction capabilities of large Convolutional Neural Networks with the potential benefits of Quantum Machine Learning (QML).
Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures
We propose a novel hybrid architecture: instead of utilizing a pre-trained network for compression, we employ an autoencoder to derive a compressed version of the input data.
Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization
We showed that our Variational Quantum Circuit approaches perform significantly better compared to a neural network with a similar amount of trainable parameters.
Applying QNLP to sentiment analysis in finance
As an application domain where the slightest qualitative improvements can yield immense value, finance is a promising candidate for early quantum advantage.
Improving Primate Sounds Classification using Binary Presorting for Deep Learning
In the field of wildlife observation and conservation, approaches involving machine learning on audio recordings are becoming increasingly popular.
Weight Re-Mapping for Variational Quantum Algorithms
Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum machine learning applications.
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing
Current state-of-the-art approaches for generalization apply data augmentation techniques to increase the diversity of training data.
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training
We propose discriminative reward co-training (DIRECT) as an extension to deep reinforcement learning algorithms.
Compression of GPS Trajectories using Autoencoders
The ubiquitous availability of mobile devices capable of location tracking led to a significant rise in the collection of GPS data.
SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training
However, as high-dimensional real-world applications are not yet feasible to be solved using purely quantum hardware, hybrid methods using both classical and quantum machine learning paradigms have been proposed.
Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping
In recent years, quantum machine learning has seen a substantial increase in the use of variational quantum circuits (VQCs).
Empirical Analysis of Limits for Memory Distance in Recurrent Neural Networks
Common to all different kinds of recurrent neural networks (RNNs) is the intention to model relations between data points through time.
VoronoiPatches: Evaluating A New Data Augmentation Method
We primarily utilize non-linear recombination of information within an image, fragmenting and occluding small information patches.
Constructing Organism Networks from Collaborative Self-Replicators
We introduce organism networks, which function like a single neural network but are composed of several neural particle networks; while each particle network fulfils the role of a single weight application within the organism network, it is also trained to self-replicate its own weights.
Capturing Dependencies within Machine Learning via a Formal Process Model
We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way.
Stochastic Market Games
Some of the most relevant future applications of multi-agent systems like autonomous driving or factories as a service display mixed-motive scenarios, where agents might have conflicting goals.
Black Box Optimization Using QUBO and the Cross Entropy Method
A common approach to realising BBO is to learn a surrogate model which approximates the target black-box function which can then be solved via white-box optimization methods.
Case-Based Inverse Reinforcement Learning Using Temporal Coherence
As a prior, we assume that the higher-level strategy is to reach an unknown target state area, which we hypothesize is a valid prior for many domains in Reinforcement Learning.
Quantifying Multimodality in World Models
In this work, we analyze existing and propose new metrics for the detection and quantification of multimodal uncertainty in RL based World Models.
VAST: Value Function Factorization with Variable Agent Sub-Teams
We evaluate VAST in three multi-agent domains and show that VAST can significantly outperform state-of-the-art VFF, when the number of agents is sufficiently large.
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement Learning
This paper considers sustainable and cooperative behavior in multi-agent systems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning
A characteristic of reinforcement learning is the ability to develop unforeseen strategies when solving problems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Acoustic Leak Detection in Water Networks
In this work, we present a general procedure for acoustic leak detection in water networks that satisfies multiple real-world constraints such as energy efficiency and ease of deployment.
Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms
In many fields of research, labeled datasets are hard to acquire.
Content-based Recommendations for Radio Stations with Deep Learned Audio Fingerprints
That is why finding stations playing the preferred content is a tough task for a potential listener, especially due to the overwhelming number of offered choices.
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning
In industrial applications, the early detection of malfunctioning factory machinery is crucial.
Policy Entropy for Out-of-Distribution Classification
One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained.
The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline
We discuss the synergetic connection between quantum computing and artificial intelligence.
Insights on Training Neural Networks for QUBO Tasks
Current hardware limitations restrict the potential when solving quadratic unconstrained binary optimization (QUBO) problems via the quantum approximate optimization algorithm (QAOA) or quantum annealing (QA).
Optimizing Geometry Compression using Quantum Annealing
The compression of geometry data is an important aspect of bandwidth-efficient data transfer for distributed 3d computer vision applications.
Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA
Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem.
Quantum Physics
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning
We further present a first viable solution for calculating a dynamic classification threshold, based on the uncertainty distribution of the training data.
Difficulty Classification of Mountainbike Downhill Trails utilizing Deep Neural Networks
The difficulty of mountainbike downhill trails is a subjective perception.
Soccer Team Vectors
In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space.
Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning
We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a general memory bounded approach to partially observable open-loop planning.
Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning
In nature, flocking or swarm behavior is observed in many species as it has beneficial properties like reducing the probability of being caught by a predator.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Memory Bounded Open-Loop Planning in Large POMDPs using Thompson Sampling
State-of-the-art approaches to partially observable planning like POMCP are based on stochastic tree search.
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies
We experimentally evaluate STEP in two challenging and stochastic domains with large state and joint action spaces and show that STEP is able to learn stronger policies than standard multi-agent reinforcement learning algorithms, when combining multi-agent open-loop planning with centralized function approximation.
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning
Although prior work has shown that dropout-based variational inference techniques and bootstrap-based approaches can be used to model epistemic uncertainty, the suitability for detecting OOD samples in deep reinforcement learning remains an open question.
Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms
Diversity is an important factor in evolutionary algorithms to prevent premature convergence towards a single local optimum.
