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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 1026–1050 of 1918 papers

TitleStatusHype
Gap-Dependent Bounds for Two-Player Markov Games—0
Towards self-organized control: Using neural cellular automata to robustly control a cart-pole agentCode1
DRILL-- Deep Reinforcement Learning for Refinement Operators in ALC—0
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples—0
Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning—0
Concentration of Contractive Stochastic Approximation and Reinforcement Learning—0
Reinforcement Learning for Mean Field Games, with Applications to Economics—0
Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality—0
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via DiscretisationCode1
IQ-Learn: Inverse soft-Q Learning for ImitationCode1
Q-Learning Lagrange Policies for Multi-Action Restless BanditsCode0
Reinforcement Learning for Physical Layer CommunicationsCode0
Distributed Heuristic Multi-Agent Path Finding with CommunicationCode1
Reinforcement Learning for Resource Allocation in Steerable Laser-based Optical Wireless Systems—0
Analytically Tractable Bayesian Deep Q-Learning—0
Boosting Offline Reinforcement Learning with Residual Generative Modeling—0
Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes—0
A Deep Reinforcement Learning Approach towards Pendulum Swing-up Problem based on TF-Agents—0
A Q-Learning-Based Topology-Aware Routing Protocol for Flying Ad Hoc Networks—0
Unbiased Methods for Multi-Goal Reinforcement Learning—0
Efficient (Soft) Q-Learning for Text Generation with Limited Good DataCode1
TempoRL: Learning When to ActCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Decentralized Q-Learning in Zero-sum Markov Games—0
Bridging the Gap Between Target Networks and Functional RegularizationCode0
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