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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 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.
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).
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.
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.
