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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

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Showing 1401–1450 of 1918 papers

TitleStatusHype
Q-Learning in Regularized Mean-field Games—0
Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation—0
Deep Constrained Q-learning—0
Deep Reinforcement Learning with Weighted Q-Learning—0
Active Perception and Representation for Robotic Manipulation—0
Application of Deep Q-Network in Portfolio Management—0
A General Framework for Learning Mean-Field Games—0
Provably Efficient Model-Free Algorithm for MDPs with Peak Constraints—0
Indirect and Direct Training of Spiking Neural Networks for End-to-End Control of a Lane-Keeping Vehicle—0
Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning—0
Software-Level Accuracy Using Stochastic Computing With Charge-Trap-Flash Based Weight Matrix—0
A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles—0
Transfer Reinforcement Learning under Unobserved Contextual Information—0
Reinforcement Learning Based Cooperative Coded Caching under Dynamic Popularities in Ultra-Dense Networks—0
Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning—0
Adaptive Structural Hyper-Parameter Configuration by Q-Learning—0
Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts—0
Deep Reinforcement Learning for FlipIt Security Game—0
ConQUR: Mitigating Delusional Bias in Deep Q-learningCode0
G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning—0
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization—0
A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint—0
Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning—0
Q-learning with Uniformly Bounded Variance: Large Discounting is Not a Barrier to Fast Learning—0
Periodic Q-Learning—0
Anypath Routing Protocol Design via Q-Learning for Underwater Sensor Networks—0
UAV Aided Search and Rescue Operation Using Reinforcement Learning—0
Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity—0
Listwise Learning to Rank with Deep Q-Networks—0
Regret Bounds for Discounted MDPs—0
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis—0
GLSearch: Maximum Common Subgraph Detection via Learning to Search—0
Learning State Abstractions for Transfer in Continuous ControlCode0
Manipulating Reinforcement Learning: Poisoning Attacks on Cost Signals—0
Safe Wasserstein Constrained Deep Q-Learning—0
Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes—0
Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-Learning—0
Autonomous Control of a Line Follower Robot Using a Q-Learning Controller—0
Q-Learning in enormous action spaces via amortized approximate maximization—0
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping—0
A storage expansion planning framework using reinforcement learning and simulation-based optimization—0
A Probabilistic Simulator of Spatial Demand for Product Allocation—0
EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning—0
Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar—0
Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog—0
SVQN: Sequential Variational Soft Q-Learning Networks—0
Information Theoretic Model Predictive Q-Learning—0
Learning in Discounted-cost and Average-cost Mean-field Games—0
The Gambler's Problem and Beyond—0
Learning an Interpretable Traffic Signal Control PolicyCode0
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