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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 1326–1350 of 1918 papers

TitleStatusHype
Navigation with QPHIL: Quantizing Planner for Hierarchical Implicit Q-Learning—0
Near-Optimal Regret Bounds for Model-Free RL in Non-Stationary Episodic MDPs—0
Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control—0
Near-Optimal Reinforcement Learning with Self-Play—0
Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning—0
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations—0
Networked Control of Nonlinear Systems under Partial Observation Using Continuous Deep Q-Learning—0
Hyperparameter optimization with REINFORCE and Transformers—0
Neural-Kernel Conditional Mean Embeddings—0
Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction—0
Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning—0
Neural networks with motivation—0
Neural Q-learning for solving PDEs—0
Neural Temporal-Difference Learning Converges to Global Optima—0
Neurohex: A Deep Q-learning Hex Agent—0
Neuromimetic Linear Systems -- Resilience and Learning—0
Non-Asymptotic Guarantees for Average-Reward Q-Learning with Adaptive Stepsizes—0
Non-delusional Q-learning and value-iteration—0
No-Regret Reinforcement Learning with Heavy-Tailed Rewards—0
Numeric Reward Machines—0
Object Goal Navigation using Data Regularized Q-Learning—0
Off-line approximate dynamic programming for the vehicle routing problem with a highly variable customer basis and stochastic demands—0
Offline Decentralized Multi-Agent Reinforcement Learning—0
Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit—0
OER: Offline Experience Replay for Continual Offline Reinforcement Learning—0
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