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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 10011050 of 1918 papers

TitleStatusHype
An Independent Study of Reinforcement Learning and Autonomous Driving0
DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks0
Maximizing Influence with Graph Neural Networks0
Modified Double DQN: addressing stability0
An Elementary Proof that Q-learning Converges Almost Surely0
Offline Decentralized Multi-Agent Reinforcement Learning0
SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous RobotsCode0
A DQN-based Approach to Finding Precise Evidences for Fact VerificationCode0
A Distributed Intelligence Architecture for B5G Network Automation0
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings0
Double Deep Q-learning Based Real-Time Optimization Strategy for Microgrids0
Integrating Deep Learning and Augmented Reality to Enhance Situational Awareness in Firefighting Environments0
Constraints Penalized Q-learning for Safe Offline Reinforcement Learning0
A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens0
Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication0
Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things0
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem0
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy LearningCode0
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement LearningCode0
The Least Restriction for Offline Reinforcement Learning0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments0
Markov Decision Process modeled with Bandits for Sequential Decision Making in Linear-flow0
Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data AugmentationCode1
Distilling Reinforcement Learning Tricks for Video GamesCode1
Gap-Dependent Bounds for Two-Player Markov Games0
Towards self-organized control: Using neural cellular automata to robustly control a cart-pole agentCode1
DRILL-- Deep Reinforcement Learning for Refinement Operators in ALC0
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples0
Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning0
Concentration of Contractive Stochastic Approximation and Reinforcement Learning0
Reinforcement Learning for Mean Field Games, with Applications to Economics0
Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality0
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 Systems0
Analytically Tractable Bayesian Deep Q-Learning0
Boosting Offline Reinforcement Learning with Residual Generative Modeling0
Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes0
A Deep Reinforcement Learning Approach towards Pendulum Swing-up Problem based on TF-Agents0
A Q-Learning-Based Topology-Aware Routing Protocol for Flying Ad Hoc Networks0
Unbiased Methods for Multi-Goal Reinforcement Learning0
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 Games0
Bridging the Gap Between Target Networks and Functional RegularizationCode0
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