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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 1701–1750 of 1918 papers

TitleStatusHype
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy CriticCode0
Factors of Influence of the Overestimation Bias of Q-LearningCode0
MacLight: Multi-scene Aggregation Convolutional Learning for Traffic Signal ControlCode0
ConQUR: Mitigating Delusional Bias in Deep Q-learningCode0
DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather DataCode0
Making Deep Q-learning methods robust to time discretizationCode0
Bootstrapped Meta-LearningCode0
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy LearningCode0
Deep Q-learning from DemonstrationsCode0
Reinforcement Learning with Low-Complexity Liquid State MachinesCode0
The Effects of Memory Replay in Reinforcement LearningCode0
Compressed Federated Reinforcement Learning with a Generative ModelCode0
Deep Q-Learning for Nash Equilibria: Nash-DQNCode0
Mastering Percolation-like Games with Deep LearningCode0
Towards Symbolic Reinforcement Learning with Common SenseCode0
A Novel Update Mechanism for Q-Networks Based On Extreme Learning MachinesCode0
Deep Q learning for fooling neural networksCode0
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion DetectionCode0
An intelligent financial portfolio trading strategy using deep Q-learningCode0
Efficient Parallel Reinforcement Learning Framework using the Reactor ModelCode0
A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility ServicesCode0
Remember and Forget for Experience ReplayCode0
Meta-Black-Box-Optimization through Offline Q-function LearningCode0
UNIQ: Offline Inverse Q-learning for Avoiding Undesirable DemonstrationsCode0
Meta-Q-LearningCode0
Meta-Value Learning: a General Framework for Learning with Learning AwarenessCode0
Adversarial Learning of a Sampler Based on an Unnormalized DistributionCode0
Deep Q-learning: a robust control approachCode0
Deep Ordinal Reinforcement LearningCode0
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted RegressionCode0
Orchestrated Value Mapping for Reinforcement LearningCode0
Angrier Birds: Bayesian reinforcement learningCode0
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement LearningCode0
Offline Contextual Bandits with Overparameterized ModelsCode0
An Empirical Study of Deep Reinforcement Learning in Continuing TasksCode0
Simulation of Nanorobots with Artificial Intelligence and Reinforcement Learning for Advanced Cancer Cell Detection and TrackingCode0
PairVDN - Pair-wise Decomposed Value FunctionsCode0
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement LearningCode0
Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic MethodsCode0
Mixed-Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Electric Vehicle Energy ManagementCode0
Variation-resistant Q-learning: Controlling and Utilizing Estimation Bias in Reinforcement Learning for Better PerformanceCode0
Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel SimulationCode0
Parameter-free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy GradientsCode0
RadDQN: a Deep Q Learning-based Architecture for Finding Time-efficient Minimum Radiation Exposure PathwayCode0
Boosting Soft Q-Learning by BoundingCode0
Automaton-Guided Curriculum Generation for Reinforcement Learning AgentsCode0
Variations on the Reinforcement Learning performance of BlackjackCode0
Model-Free Adaptive Optimal Control of Episodic Fixed-Horizon Manufacturing Processes using Reinforcement LearningCode0
Deterministic Implementations for Reproducibility in Deep Reinforcement LearningCode0
Designing Neural Network Architectures using Reinforcement LearningCode0
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