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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 1151–1175 of 1918 papers

TitleStatusHype
Blackwell Online Learning for Markov Decision Processes—0
POPO: Pessimistic Offline Policy OptimizationCode0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
Goal Reasoning by Selecting Subgoals with Deep Q-Learning—0
Distributed Q-Learning with State Tracking for Multi-agent Networked Control—0
Stabilizing Q Learning Via Soft Mellowmax Operator—0
Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FELCode0
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation—0
Deploying Reinforcement Learning in Water Transport—0
Virtual Autonomous Driving with Reinforcement Learning—0
Semi-Supervised Off Policy Reinforcement Learning—0
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation—0
Amortized Q-learning with Model-based Action Proposals for Autonomous Driving on Highways—0
Hippocampal representations emerge when training recurrent neural networks on a memory dependent maze navigation task—0
Self-correcting Q-Learning—0
Agnostic Q-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity—0
A new convergent variant of Q-learning with linear function approximation—0
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory—0
A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms—0
Robust Multi-Agent Reinforcement Learning with Model Uncertainty—0
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles—0
Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning—0
Reinforcement Learning-based Joint Path and Energy Optimization of Cellular-Connected Unmanned Aerial Vehicles—0
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