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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 376–400 of 1918 papers

TitleStatusHype
Value function interference and greedy action selection in value-based multi-objective reinforcement learning—0
Attention-Enhanced Prioritized Proximal Policy Optimization for Adaptive Edge Caching—0
Enhancement of High-definition Map Update Service Through Coverage-aware and Reinforcement Learning—0
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices—0
Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy OptimizationCode0
A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks—0
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning AgentsCode0
Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs—0
MinMaxMin Q-learning—0
SQT -- std Q-target—0
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-errorCode1
DRL-Based Dynamic Channel Access and SCLAR Maximization for Networks Under Jamming—0
Deep Robot Sketching: An application of Deep Q-Learning Networks for human-like sketching—0
RadDQN: a Deep Q Learning-based Architecture for Finding Time-efficient Minimum Radiation Exposure PathwayCode0
FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game—0
Nash Soft Actor-Critic LEO Satellite Handover Management Algorithm for Flying Vehicles—0
Scheduled Curiosity-Deep Dyna-Q: Efficient Exploration for Dialog Policy Learning—0
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator—0
Emergence of cooperation under punishment: A reinforcement learning perspective—0
Regularized Q-Learning with Linear Function Approximation—0
Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation—0
Information-Theoretic State Variable Selection for Reinforcement LearningCode0
VQC-Based Reinforcement Learning with Data Re-uploading: Performance and TrainabilityCode0
REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes—0
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