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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

Showing 151200 of 1918 papers

TitleStatusHype
Optimistic Exploration even with a Pessimistic InitialisationCode1
Optimistic Multi-Agent Policy GradientCode1
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via DiscretisationCode1
Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial PuzzlesCode0
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill DiscoveryCode0
Autoequivariant Network Search via Group DecompositionCode0
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RLCode0
Hierarchical Reinforcement Learning with the MAXQ Value Function DecompositionCode0
Goal Recognition as Reinforcement LearningCode0
Graph Backup: Data Efficient Backup Exploiting Markovian TransitionsCode0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement LearningCode0
Generating a Graph Colouring Heuristic with Deep Q-Learning and Graph Neural NetworksCode0
Goal-Conditioned Q-Learning as Knowledge DistillationCode0
Group Equivariant Deep Reinforcement LearningCode0
From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no LibrariesCode0
GAN Q-learningCode0
Generalized Speedy Q-learningCode0
Agent Performing Autonomous Stock Trading under Good and Bad SituationsCode0
Adaptive Discretization for Episodic Reinforcement Learning in Metric SpacesCode0
Factors of Influence of the Overestimation Bias of Q-LearningCode0
Stabilizing Off-Policy Q-Learning via Bootstrapping Error ReductionCode0
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement LearningCode0
Generalized Value Iteration Networks: Life Beyond LatticesCode0
AFU: Actor-Free critic Updates in off-policy RL for continuous controlCode0
Evolution of cooperation in a bimodal mixture of conditional cooperatorsCode0
A Framework for Automated Cellular Network Tuning with Reinforcement LearningCode0
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic MethodsCode0
Examining Policy Entropy of Reinforcement Learning Agents for Personalization TasksCode0
Enhancing Robot Assistive Behaviour with Reinforcement Learning and Theory of MindCode0
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy ImprovementCode0
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement LearningCode0
Efficient Sparse-Reward Goal-Conditioned Reinforcement Learning with a High Replay Ratio and RegularizationCode0
Efficient Model-free Reinforcement Learning in Metric SpacesCode0
Explainable and Safe Reinforcement Learning for Autonomous Air MobilityCode0
Dynamic control of self-assembly of quasicrystalline structures through reinforcement learningCode0
A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility ServicesCode0
DynamicLight: Two-Stage Dynamic Traffic Signal TimingCode0
Adversarial Learning of a Sampler Based on an Unnormalized DistributionCode0
Dual Ensembled Multiagent Q-Learning with Hypernet RegularizerCode0
Double Q-PID algorithm for mobile robot controlCode0
Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement LearningCode0
Active exploration in parameterized reinforcement learningCode0
Diagnosing Bottlenecks in Deep Q-learning AlgorithmsCode0
DRL4AOI: A DRL Framework for Semantic-aware AOI Segmentation in Location-Based ServicesCode0
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet ManagementCode0
Exploring reinforcement learning techniques for discrete and continuous control tasks in the MuJoCo environmentCode0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
Distributionally Robust Deep Q-LearningCode0
A Novel Update Mechanism for Q-Networks Based On Extreme Learning MachinesCode0
Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze ProblemsCode0
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