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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 651–700 of 1918 papers

TitleStatusHype
Offline Reinforcement Learning with Imbalanced Datasets—0
LLQL: Logistic Likelihood Q-Learning for Reinforcement Learning—0
Elastic Decision Transformer—0
Stability of Q-Learning Through Design and Optimism—0
Achieving Stable Training of Reinforcement Learning Agents in Bimodal Environments through Batch Learning—0
Is Risk-Sensitive Reinforcement Learning Properly Resolved?—0
Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization PerspectiveCode0
Continuous-time q-learning for mean-field control problems—0
Evaluation of Reinforcement Learning Techniques for Trading on a Diverse Portfolio—0
Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning—0
RansomAI: AI-powered Ransomware for Stealthy Encryption—0
Decentralized Multi-Robot Formation Control Using Reinforcement Learning—0
Action Q-Transformer: Visual Explanation in Deep Reinforcement Learning with Encoder-Decoder Model using Action Query—0
Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback—0
Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation—0
Vanishing Bias Heuristic-guided Reinforcement Learning Algorithm—0
Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement LearningCode0
Residual Q-Learning: Offline and Online Policy Customization without Value—0
Privacy Risks in Reinforcement Learning for Household Robots—0
Algorithmic Collusion in Auctions: Evidence from Controlled Laboratory Experiments—0
Model-based versus model-free feeding control and water quality monitoring for fish growth tracking in aquaculture systems—0
Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care—0
Approximate information state based convergence analysis of recurrent Q-learning—0
Finite-Time Analysis of Minimax Q-Learning for Two-Player Zero-Sum Markov Games: Switching System Approach—0
Active Inference in Hebbian Learning Networks—0
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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