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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 101150 of 1918 papers

TitleStatusHype
Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised LearningCode1
Research on Robot Path Planning Based on Reinforcement LearningCode1
Reward Machines for Cooperative Multi-Agent Reinforcement LearningCode1
Extreme Q-Learning: MaxEnt RL without EntropyCode1
GAIL-PT: A Generic Intelligent Penetration Testing Framework with Generative Adversarial Imitation LearningCode1
Robust Q-learning Algorithm for Markov Decision Processes under Wasserstein UncertaintyCode1
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
A Search-Based Testing Approach for Deep Reinforcement Learning AgentsCode1
Deep Recurrent Q-Learning for Partially Observable MDPsCode1
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-LearningCode1
Adaptive Contention Window Design using Deep Q-learningCode1
Deep Reinforcement Learning with Double Q-learningCode1
Spatial Action Maps for Mobile ManipulationCode1
Split Q Learning: Reinforcement Learning with Two-Stream RewardsCode1
Dropout Q-Functions for Doubly Efficient Reinforcement LearningCode1
Strategically Conservative Q-LearningCode1
An Optimistic Perspective on Offline Reinforcement LearningCode1
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement LearningCode1
Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial DetectionCode1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
Efficient (Soft) Q-Learning for Text Generation with Limited Good DataCode1
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-errorCode1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement LearningCode1
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological ModelsCode1
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Gradient Temporal-Difference Learning with Regularized CorrectionsCode1
Learning the Markov Decision Process in the Sparse Gaussian EliminationCode1
Multi-Agent Determinantal Q-LearningCode1
Energy-based Surprise Minimization for Multi-Agent Value FactorizationCode1
A Recipe for Unbounded Data Augmentation in Visual Reinforcement LearningCode1
Evolution Strategies as a Scalable Alternative to Reinforcement LearningCode1
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning TechniquesCode1
Randomized Ensembled Double Q-Learning: Learning Fast Without a ModelCode1
Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous ControlsCode1
A Stochastic Game Framework for Efficient Energy Management in Microgrid NetworksCode1
Addressing Function Approximation Error in Actor-Critic MethodsCode1
Hybrid RL: Using Both Offline and Online Data Can Make RL EfficientCode1
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
IQ-Learn: Inverse soft-Q Learning for ImitationCode1
Is Q-learning Provably Efficient?Code1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Benchmarking Batch Deep Reinforcement Learning AlgorithmsCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
MADiff: Offline Multi-agent Learning with Diffusion ModelsCode1
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19Code1
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
Boosting Continuous Control with Consistency PolicyCode1
Uncertainty Weighted Actor-Critic for Offline Reinforcement LearningCode1
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