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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 801–825 of 1918 papers

TitleStatusHype
Double Deep Q-Learning in Opponent Modeling—0
Learning Self-Awareness Models for Physical Layer Security in Cognitive and AI-enabled Radios—0
Reinforcement Causal Structure Learning on Order Graph—0
Simultaneously Updating All Persistence Values in Reinforcement Learning—0
Examining Policy Entropy of Reinforcement Learning Agents for Personalization TasksCode0
Credit-cognisant reinforcement learning for multi-agent cooperation—0
Analysis of Reinforcement Learning Schemes for Trajectory Optimization of an Aerial Radio Unit—0
A Reinforcement Learning Approach for Process Parameter Optimization in Additive Manufacturing—0
Planning Irregular Object Packing via Hierarchical Reinforcement Learning—0
Addressing the issue of stochastic environments and local decision-making in multi-objective reinforcement learning—0
On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization—0
Exploratory Control with Tsallis Entropy for Latent Factor Models—0
Reinforcement Learning in Non-Markovian Environments—0
DynamicLight: Two-Stage Dynamic Traffic Signal TimingCode0
Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems—0
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints—0
Quantum deep recurrent reinforcement learning—0
Attitude Control of Highly Maneuverable Aircraft Using an Improved Q-learning—0
Sufficient Exploration for Convex Q-learning—0
Model-Free Characterizations of the Hamilton-Jacobi-Bellman Equation and Convex Q-Learning in Continuous Time—0
Mutual Information Regularized Offline Reinforcement LearningCode0
Deep reinforcement learning for automatic run-time adaptation of UWB PHY radio settings—0
Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources using Autonomous Surface Vehicles—0
DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System—0
Factors of Influence of the Overestimation Bias of Q-LearningCode0
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