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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 1001–1050 of 1918 papers

TitleStatusHype
Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning—0
Projection Implicit Q-Learning with Support Constraint for Offline Reinforcement Learning—0
Projective simulation for classical learning agents: a comprehensive investigation—0
Prospect-theoretic Q-learning—0
Prospect Theory-inspired Automated P2P Energy Trading with Q-learning-based Dynamic Pricing—0
Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning—0
Provable Multi-Objective Reinforcement Learning with Generative Models—0
Provable Reinforcement Learning for Networked Control Systems with Stochastic Packet Disordering—0
Gauss-Newton Temporal Difference Learning with Nonlinear Function Approximation—0
Provably Efficient Kernelized Q-Learning—0
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication—0
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle—0
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle—0
Provably Efficient Q-Learning with Low Switching Cost—0
Provably Efficient Reinforcement Learning with Aggregated States—0
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games—0
Provably More Efficient Q-Learning in the One-Sided-Feedback/Full-Feedback Settings—0
Direct Data-Driven Discrete-time Bilinear Biquadratic Regulator—0
Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care—0
Pseudorehearsal in value function approximation—0
Learned Collusion—0
Q-Cogni: An Integrated Causal Reinforcement Learning Framework—0
Q-CP: Learning Action Values for Cooperative Planning—0
Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning—0
QF-tuner: Breaking Tradition in Reinforcement Learning—0
Qgraph-bounded Q-learning: Stabilizing Model-Free Off-Policy Deep Reinforcement Learning—0
Q-greedyUCB: a New Exploration Policy for Adaptive and Resource-efficient Scheduling—0
Efficient Off-Policy Reinforcement Learning via Brain-Inspired Computing—0
QLAMMP: A Q-Learning Agent for Optimizing Fees on Automated Market Making Protocols—0
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes—0
Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells—0
Q-learning as a monotone scheme—0
Q-learning Assisted Energy-Aware Traffic Offloading and Cell Switching in Heterogeneous Networks—0
Q-Learning Based Aerial Base Station Placement for Fairness Enhancement in Mobile Networks—0
Q-learning-based Hierarchical Cooperative Local Search for Steelmaking-continuous Casting Scheduling Problem—0
Q-learning-based Model-free Safety Filter—0
Q-learning Based Optimal False Data Injection Attack on Probabilistic Boolean Control Networks—0
Q-Learning based system for path planning with unmanned aerial vehicles swarms in obstacle environments—0
Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL—0
A Distributed Intelligence Architecture for B5G Network Automation—0
Q-Learning for Continuous Actions with Cross-Entropy Guided Policies—0
Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity—0
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis—0
Q-learning for Optimal Control of Continuous-time Systems—0
Q-learning for POMDP: An application to learning locomotion gaits—0
Q-learning for real time control of heterogeneous microagent collectives—0
Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environments—0
q-Learning in Continuous Time—0
Q-Learning in enormous action spaces via amortized approximate maximization—0
Q-Learning in Regularized Mean-field Games—0
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