Dueling Network Architectures for Deep Reinforcement Learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas
The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.
this paper
works it cites
works citing it
node size = global citations · hover for the full title
Citation edge evidence
- Deep Reinforcement Learning: A Brief Survey→Dueling Network Architectures for Deep Reinforcement LearningLegacy edge — source detail unavailable
- Sigmoid-weighted linear units for neural network function approximation in reinforcement …→Dueling Network Architectures for Deep Reinforcement LearningLegacy edge — source detail unavailable
- Rainbow: Combining Improvements in Deep Reinforcement Learning→Dueling Network Architectures for Deep Reinforcement LearningLegacy edge — source detail unavailable
- Dueling Network Architectures for Deep Reinforcement Learning→Mastering the game of Go with deep neural networks and tree searchLegacy edge — source detail unavailable
- Dueling Network Architectures for Deep Reinforcement Learning→Introduction to Reinforcement LearningLegacy edge — source detail unavailable
- Dueling Network Architectures for Deep Reinforcement Learning→Policy Gradient Methods for Reinforcement Learning with Function ApproximationLegacy edge — source detail unavailable
- Dueling Network Architectures for Deep Reinforcement Learning→Deep Reinforcement Learning with Double Q-LearningLegacy edge — source detail unavailable
- Dueling Network Architectures for Deep Reinforcement Learning→End-to-End Training of Deep Visuomotor PoliciesLegacy edge — source detail unavailable
- Policy Gradient Methods for Reinforcement Learning with Function Approximation→Introduction to Reinforcement LearningLegacy edge — source detail unavailable
- Deep Reinforcement Learning with Double Q-Learning→Introduction to Reinforcement LearningLegacy edge — source detail unavailable
- Mastering the game of Go with deep neural networks and tree search→Policy Gradient Methods for Reinforcement Learning with Function ApproximationLegacy edge — source detail unavailable
- Rainbow: Combining Improvements in Deep Reinforcement Learning→Deep Reinforcement Learning with Double Q-LearningLegacy edge — source detail unavailable
- Sigmoid-weighted linear units for neural network function approximation in reinforcement …→Mastering the game of Go with deep neural networks and tree searchLegacy edge — source detail unavailable
- Deep Reinforcement Learning: A Brief Survey→End-to-End Training of Deep Visuomotor PoliciesLegacy edge — source detail unavailable
- Deep Reinforcement Learning: A Brief Survey→Deep Reinforcement Learning with Double Q-LearningLegacy edge — source detail unavailable
- Deep Reinforcement Learning: A Brief Survey→Mastering the game of Go with deep neural networks and tree searchLegacy edge — source detail unavailable
What this paper cites, inside the corpus
What cites it, inside the corpus
Links
Download BibTeX · Download RIS · DOI · Open access copy · OpenAlex record
Topics
| Reinforcement Learning in Robotics | Computer Science |
| Adversarial Robustness in Machine Learning | Computer Science |
| Autonomous Vehicle Technology and Safety | Engineering |
Is this record sound?
complete
Nothing in this record contradicts itself and no field we check is missing.
- supports6 author record(s) attached.
- supports23 reference(s) recorded.
- neutralThe DOI carries no year to check against.
- supportsA title is present.
Provenance
sha256 db1645b78a57e29a…