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Found 14 papers, 3 papers with code
A Case for Validation Buffer in Pessimistic Actor-Critic
VPL uses a small validation buffer to adjust the levels of pessimism throughout the agent training, with the pessimism set such that the approximation error of the critic targets is minimized.
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning
We tested these agents across 14 diverse tasks from 2 simulation benchmarks.
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models.
Curriculum reinforcement learning for quantum architecture search under hardware errors
In the case of parameter optimization alone, noise effects have been observed to dramatically influence the performance of the optimizer and final outcomes, which is a key line of study.
On consequences of finetuning on data with highly discriminative features
In the era of transfer learning, training neural networks from scratch is becoming obsolete.
Enhancing variational quantum state diagonalization using reinforcement learning techniques
We demonstrate that the circuits proposed by the reinforcement learning methods are shallower than the standard variational quantum state diagonalization algorithm and thus can be used in situations where hardware capabilities limit the depth of quantum circuits.
The Effectiveness of World Models for Continual Reinforcement Learning
World models power some of the most efficient reinforcement learning algorithms.
Emergency action termination for immediate reaction in hierarchical reinforcement learning
In this paper, we address this gap in the state-of-the-art approaches and propose a method in which the validity of higher-level actions (thus lower-level goals) is constantly verified at the higher level.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Reinforcement learning with experience replay and adaptation of action dispersion
To that end, the dispersion should be tuned to assure a sufficiently high probability (densities) of the actions in the replay buffer and the modes of the distributions that generated them, yet this dispersion should not be higher.
Reinforcement learning for optimization of variational quantum circuit architectures
The study of Variational Quantum Eigensolvers (VQEs) has been in the spotlight in recent times as they may lead to real-world applications of near-term quantum devices.
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling
This approach can be applied to many of the hyperspectral classification problems.
General Classification
Hyperspectral Image Classification
+2
Structure optimization for parameterized quantum circuits
We demonstrate the method for optimizing a variational quantum eigensolver for finding the ground states of Lithium Hydride and the Heisenberg model in simulation, and for finding the ground state of Hydrogen gas on the IBM Melbourne quantum computer.
Quantum Physics
QSWalk.jl: Julia package for quantum stochastic walks analysis
The presented paper describes QSWalk. jl package for Julia programming language, developed for the purpose of simulating the evolution of open quantum systems.
Quantum Physics
Quantum image classification using principal component analysis
We present a novel quantum algorithm for classification of images.
