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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 201–250 of 1918 papers

TitleStatusHype
Enhancing Robot Assistive Behaviour with Reinforcement Learning and Theory of MindCode0
Exploring reinforcement learning techniques for discrete and continuous control tasks in the MuJoCo environmentCode0
A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility ServicesCode0
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic MethodsCode0
DynamicLight: Two-Stage Dynamic Traffic Signal TimingCode0
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithmsCode0
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet ManagementCode0
Dual Ensembled Multiagent Q-Learning with Hypernet RegularizerCode0
Dynamic control of self-assembly of quasicrystalline structures through reinforcement learningCode0
Efficient Model-free Reinforcement Learning in Metric SpacesCode0
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy ImprovementCode0
GAN Q-learningCode0
Double Q-PID algorithm for mobile robot controlCode0
Generalized Value Iteration Networks: Life Beyond LatticesCode0
Distributionally Robust Deep Q-LearningCode0
Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement LearningCode0
A Framework for Automated Cellular Network Tuning with Reinforcement LearningCode0
Group Equivariant Deep Reinforcement LearningCode0
Active exploration in parameterized reinforcement learningCode0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
AFU: Actor-Free critic Updates in off-policy RL for continuous controlCode0
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill DiscoveryCode0
DRL4AOI: A DRL Framework for Semantic-aware AOI Segmentation in Location-Based ServicesCode0
Efficient Sparse-Reward Goal-Conditioned Reinforcement Learning with a High Replay Ratio and RegularizationCode0
Stabilizing Off-Policy Q-Learning via Bootstrapping Error ReductionCode0
Implications of Decentralized Q-learning Resource Allocation in Wireless NetworksCode0
A Self-Adaptive Proposal Model for Temporal Action Detection based on Reinforcement LearningCode0
A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based ApplicationsCode0
Agent Performing Autonomous Stock Trading under Good and Bad SituationsCode0
Information-Theoretic State Variable Selection for Reinforcement LearningCode0
Inverse Q-Learning Done Right: Offline Imitation Learning in Q^π-Realizable MDPsCode0
Investigating the Performance and Reliability, of the Q-Learning Algorithm in Various Unknown EnvironmentsCode0
Mastering Percolation-like Games with Deep LearningCode0
Assessing the Potential of Classical Q-learning in General Game PlayingCode0
Assumed Density Filtering Q-learningCode0
Diagnosing Bottlenecks in Deep Q-learning AlgorithmsCode0
A Novel Update Mechanism for Q-Networks Based On Extreme Learning MachinesCode0
Deterministic Implementations for Reproducibility in Deep Reinforcement LearningCode0
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted RegressionCode0
DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic NavigationCode0
Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy ApproachCode0
Learning Simple Algorithms from ExamplesCode0
A DQN-based Approach to Finding Precise Evidences for Fact VerificationCode0
Learning to Communicate with Deep Multi-Agent Reinforcement LearningCode0
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality TighteningCode0
Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic ArmCode0
Active inference: demystified and comparedCode0
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning AgentsCode0
Introspective Experience Replay: Look Back When SurprisedCode0
Active Collection of Well-Being and Health Data in Mobile DevicesCode0
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