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Q-Learning

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

Papers

Showing 12011250 of 1918 papers

TitleStatusHype
Robust Android Malware Detection System against Adversarial Attacks using Q-Learning0
Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETs0
Robust Auto-landing Control of an agile Regional Jet Using Fuzzy Q-learning0
Exploring the Noise Resilience of Successor Features and Predecessor Features Algorithms in One and Two-Dimensional Environments0
Robust Deep Reinforcement Learning with Adversarial Attacks0
Robust Multi-Agent Reinforcement Learning with Model Uncertainty0
Robust Path Following on Rivers Using Bootstrapped Reinforcement Learning0
Robust Q-learning0
RP-DQN: An application of Q-Learning to Vehicle Routing Problems0
RSS-Based Q-Learning for Indoor UAV Navigation0
Runtime Adaptation in Wireless Sensor Nodes Using Structured Learning0
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning0
Safe Coupled Deep Q-Learning for Recommendation Systems0
Safe Learning for Near Optimal Scheduling0
Safe Q-learning for continuous-time linear systems0
Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?0
Safety-guaranteed Reinforcement Learning based on Multi-class Support Vector Machine0
Safe Wasserstein Constrained Deep Q-Learning0
SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes0
Sales Time Series Analytics Using Deep Q-Learning0
Same-Day Delivery with Fairness0
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction0
Sample Complexity of Kernel-Based Q-Learning0
Sample Complexity of Variance-reduced Distributionally Robust Q-learning0
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model0
Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks0
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation0
Sample-Optimal Parametric Q-Learning Using Linearly Additive Features0
SAPO-RL: Sequential Actuator Placement Optimization for Fuselage Assembly via Reinforcement Learning0
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation0
^2-exploration for Reinforcement Learning0
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning0
Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility0
Scale-invariant temporal history (SITH): optimal slicing of the past in an uncertain world0
Scheduled Curiosity-Deep Dyna-Q: Efficient Exploration for Dialog Policy Learning0
ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks0
Search For Deep Graph Neural Networks0
Seasonal Station-Keeping of Short Duration High Altitude Balloons using Deep Reinforcement Learning0
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation0
Self-correcting Q-Learning0
Self-driving scale car trained by Deep reinforcement learning0
Self-Imitation Learning via Generalized Lower Bound Q-learning0
Self-Inspection Method of Unmanned Aerial Vehicles in Power Plants Using Deep Q-Network Reinforcement Learning0
Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning0
Self-Supervised Reinforcement Learning for Recommender Systems0
Self-Sustaining Multiple Access with Continual Deep Reinforcement Learning for Dynamic Metaverse Applications0
Semantic-Aware Remote Estimation of Multiple Markov Sources Under Constraints0
Semi-Supervised Off Policy Reinforcement Learning0
Sepsis World Model: A MIMIC-based OpenAI Gym "World Model" Simulator for Sepsis Treatment0
Sequential Learning-based IaaS Composition0
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