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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 1751–1800 of 1918 papers

TitleStatusHype
UCB Momentum Q-learning: Correcting the bias without forgettingCode0
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient LearningCode0
Performing Deep Recurrent Double Q-Learning for Atari GamesCode0
Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FELCode0
Single-partition adaptive Q-learningCode0
Active inference: demystified and comparedCode0
BlockQNN: Efficient Block-wise Neural Network Architecture GenerationCode0
From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no LibrariesCode0
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement LearningCode0
Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable EnvironmentsCode0
Automatic Data Augmentation by Learning the Deterministic PolicyCode0
DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic NavigationCode0
GAN Q-learningCode0
Personalized Exercise Recommendation with Semantically-Grounded Knowledge TracingCode0
Revisiting Fundamentals of Experience ReplayCode0
Bridging the Gap Between Target Networks and Functional RegularizationCode0
Comprehensible Context-driven Text Game PlayingCode0
Generalized Speedy Q-learningCode0
Generalized Value Iteration Networks: Life Beyond LatticesCode0
Generating a Graph Colouring Heuristic with Deep Q-Learning and Graph Neural NetworksCode0
Revisiting Prioritized Experience Replay: A Value PerspectiveCode0
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement LearningCode0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement LearningCode0
Revisiting the Softmax Bellman Operator: New Benefits and New PerspectiveCode0
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment SettingsCode0
Goal-Conditioned Q-Learning as Knowledge DistillationCode0
Reward Delay Attacks on Deep Reinforcement LearningCode0
Goal Recognition as Reinforcement LearningCode0
Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization PerspectiveCode0
DeepTPI: Test Point Insertion with Deep Reinforcement LearningCode0
Composable Deep Reinforcement Learning for Robotic ManipulationCode0
Graph Backup: Data Efficient Backup Exploiting Markovian TransitionsCode0
Automata Learning meets ShieldingCode0
Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical SystemsCode0
Momentum-based Accelerated Q-learningCode0
SPRINQL: Sub-optimal Demonstrations driven Offline Imitation LearningCode0
Monte Carlo Q-learning for General Game PlayingCode0
Deep Coordination GraphsCode0
Group Equivariant Deep Reinforcement LearningCode0
Autoequivariant Network Search via Group DecompositionCode0
Multi-Agent Advisor Q-LearningCode0
Action Candidate Driven Clipped Double Q-learning for Discrete and Continuous Action TasksCode0
Playing 2048 With Reinforcement LearningCode0
Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing ProblemCode0
Deep Reinforcement Learning with a Natural Language Action SpaceCode0
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RLCode0
Decoding fairness: a reinforcement learning perspectiveCode0
Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze ProblemsCode0
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless NetworksCode0
Traffic Light Control with Reinforcement LearningCode0
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