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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 1451–1500 of 1918 papers

TitleStatusHype
Quantum Observables for continuous control of the Quantum Approximate Optimization Algorithm via Reinforcement Learning—0
Efficient Drone Mobility Support Using Reinforcement Learning—0
Asymptotics of Reinforcement Learning with Neural Networks—0
Modelling Bahdanau Attention using Election methods aided by Q-Learning—0
Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach—0
Challenging On Car Racing Problem from OpenAI gym—0
On Solving the 2-Dimensional Greedy Shooter Problem for UAVsCode0
Generalized Speedy Q-learningCode0
Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning—0
Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning—0
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement LearningCode0
ZPD Teaching Strategies for Deep Reinforcement Learning from DemonstrationsCode0
D-Point Trigonometric Path Planning based on Q-Learning in Uncertain Environments—0
Deep Q-Learning for Same-Day Delivery with Vehicles and Drones—0
Momentum-based Accelerated Q-learningCode0
Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation—0
Policy Learning for Malaria ControlCode0
Reverse Experience Replay—0
Automatic Data Augmentation by Learning the Deterministic PolicyCode0
Adaptive Discretization for Episodic Reinforcement Learning in Metric SpacesCode0
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central InferenceCode0
On the Reduction of Variance and Overestimation of Deep Q-Learning—0
Zap Q-Learning With Nonlinear Function Approximation—0
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments—0
A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning—0
Tactical Reward Shaping: Bypassing Reinforcement Learning with Strategy-Based Goals—0
Toward Synergic Learning for Autonomous Manipulation of Deformable Tissues via Surgical Robots: An Approximate Q-Learning Approach—0
Combining No-regret and Q-learningCode0
Reinforcement Learning with Structured Hierarchical Grammar Representations of Actions—0
I'm sorry Dave, I'm afraid I can't do that, Deep Q-learning from forbidden action—0
Benchmarking Batch Deep Reinforcement Learning AlgorithmsCode1
Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition—0
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping—0
Q-learning for POMDP: An application to learning locomotion gaits—0
Composite Q-learning: Multi-scale Q-function Decomposition and Separable Optimization—0
Meta-Q-LearningCode0
Deep Coordination GraphsCode0
CAQL: Continuous Action Q-Learning—0
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Visual Exploration and Energy-aware Path Planning via Reinforcement LearningCode0
QXplore: Q-Learning Exploration by Maximizing Temporal Difference Error—0
Off-policy Multi-step Q-learning—0
Modeling Fake News in Social Networks with Deep Multi-Agent Reinforcement Learning—0
Long-term planning, short-term adjustments—0
Striving for Simplicity in Off-Policy Deep Reinforcement Learning—0
CAN ALTQ LEARN FASTER: EXPERIMENTS AND THEORY—0
Policy Tree Network—0
Active inference: demystified and comparedCode0
On the Convergence of Approximate and Regularized Policy Iteration Schemes—0
Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach—0
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