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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 201–225 of 1918 papers

TitleStatusHype
Adversarial Learning of a Sampler Based on an Unnormalized DistributionCode0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
Distributionally Robust Deep Q-LearningCode0
Active exploration in parameterized reinforcement learningCode0
Enhancing Robot Assistive Behaviour with Reinforcement Learning and Theory of MindCode0
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithmsCode0
Dual Ensembled Multiagent Q-Learning with Hypernet RegularizerCode0
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic MethodsCode0
Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial PuzzlesCode0
Exploring reinforcement learning techniques for discrete and continuous control tasks in the MuJoCo environmentCode0
Learning Principle of Least Action with Reinforcement LearningCode0
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement LearningCode0
DeepTPI: Test Point Insertion with Deep Reinforcement LearningCode0
A Novel Update Mechanism for Q-Networks Based On Extreme Learning MachinesCode0
GAN Q-learningCode0
Generalized Speedy Q-learningCode0
A Framework for Automated Cellular Network Tuning with Reinforcement LearningCode0
Generating a Graph Colouring Heuristic with Deep Q-Learning and Graph Neural NetworksCode0
DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic NavigationCode0
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted RegressionCode0
AFU: Actor-Free critic Updates in off-policy RL for continuous controlCode0
Group Equivariant Deep Reinforcement LearningCode0
Active inference: demystified and comparedCode0
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy MethodsCode0
A DQN-based Approach to Finding Precise Evidences for Fact VerificationCode0
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