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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 676–700 of 1918 papers

TitleStatusHype
Agent Performing Autonomous Stock Trading under Good and Bad SituationsCode0
Reinforcement Learning-Based Control of CrazyFlie 2.X Quadrotor—0
Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task—0
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control—0
Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple ReuseCode0
VA-learning as a more efficient alternative to Q-learning—0
Sample Complexity of Variance-reduced Distributionally Robust Q-learning—0
A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market—0
Reinforcement Learning With Reward Machines in Stochastic Games—0
Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks—0
RSRM: Reinforcement Symbolic Regression Machine—0
OER: Offline Experience Replay for Continual Offline Reinforcement Learning—0
A Framework for Provably Stable and Consistent Training of Deep Feedforward Networks—0
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond—0
Bayesian Risk-Averse Q-Learning with Streaming Observations—0
Model-Free Robust Average-Reward Reinforcement Learning—0
Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning—0
Mastering Percolation-like Games with Deep LearningCode0
On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm—0
Deep Q-Learning-based Distribution Network Reconfiguration for Reliability Improvement—0
Mixed-Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Electric Vehicle Energy ManagementCode0
Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable EnvironmentsCode0
BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading—0
Safe Q-learning for continuous-time linear systems—0
Adaptive Services Function Chain Orchestration For Digital Health Twin Use Cases: Heuristic-boosted Q-Learning Approach—0
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