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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 1651–1700 of 1918 papers

TitleStatusHype
Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement LearningCode0
On Solving the 2-Dimensional Greedy Shooter Problem for UAVsCode0
Q-Learning Lagrange Policies for Multi-Action Restless BanditsCode0
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive EnvironmentsCode0
ADDQ: Adaptive Distributional Double Q-LearningCode0
Learning To Play Atari Games Using Dueling Q-Learning and Hebbian PlasticityCode0
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality TighteningCode0
Using deep Q-learning to understand the tax evasion behavior of risk-averse firmsCode0
On the Estimation Bias in Double Q-LearningCode0
Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge EvolutionCode0
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control PriorsCode0
Self Punishment and Reward Backfill for Deep Q-LearningCode0
Enhancing Robot Assistive Behaviour with Reinforcement Learning and Theory of MindCode0
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement LearningCode0
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot NavigationCode0
Double Q-PID algorithm for mobile robot controlCode0
Adaptive Symmetric Reward Noising for Reinforcement LearningCode0
Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic ArmCode0
Stochastic approximation with cone-contractive operators: Sharp _-bounds for Q-learningCode0
Distributionally Robust Deep Q-LearningCode0
Least-Squares Policy IterationCode0
Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless NetworksCode0
Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge DistillationCode0
Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement LearningCode0
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic MethodsCode0
A critical assessment of reinforcement learning methods for microswimmer navigation in complex flowsCode0
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithmsCode0
Multi-intention Inverse Q-learning for Interpretable Behavior RepresentationCode0
Reinforcement Learning with A* and a Deep HeuristicCode0
Evolution of cooperation in a bimodal mixture of conditional cooperatorsCode0
Route Planning for Last-Mile Deliveries Using Mobile Parcel Lockers: A Hybrid Q-Learning Network ApproachCode0
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning AgentsCode0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
Examining Policy Entropy of Reinforcement Learning Agents for Personalization TasksCode0
Reinforcement Learning with Deep Energy-Based PoliciesCode0
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural NetworksCode0
Reinforcement Learning with Dynamic Boltzmann Softmax UpdatesCode0
Conservative and Risk-Aware Offline Multi-Agent Reinforcement LearningCode0
Explainable and Safe Reinforcement Learning for Autonomous Air MobilityCode0
QMR:Q-learning based Multi-objective optimization Routing protocol for Flying Ad Hoc NetworksCode0
Lookahead-Bounded Q-LearningCode0
Introspective Experience Replay: Look Back When SurprisedCode0
Welfare and Fairness in Multi-objective Reinforcement LearningCode0
Low-rank State-action Value-function ApproximationCode0
Deep Quality-Value (DQV) LearningCode0
M^2DQN: A Robust Method for Accelerating Deep Q-learning NetworkCode0
Adaptive Discretization for Episodic Reinforcement Learning in Metric SpacesCode0
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based GamesCode0
Exploring reinforcement learning techniques for discrete and continuous control tasks in the MuJoCo environmentCode0
Diagnosing Bottlenecks in Deep Q-learning AlgorithmsCode0
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