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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 126–150 of 1918 papers

TitleStatusHype
Towards Robust Offline Reinforcement Learning under Diverse Data CorruptionCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
A Recipe for Unbounded Data Augmentation in Visual Reinforcement LearningCode1
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
A Stochastic Game Framework for Efficient Energy Management in Microgrid NetworksCode1
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19Code1
Boosting Continuous Control with Consistency PolicyCode1
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-TuningCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Benchmarking Batch Deep Reinforcement Learning AlgorithmsCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Addressing Function Approximation Error in Actor-Critic MethodsCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
Deep Inverse Q-learning with ConstraintsCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissionsCode1
Deep Reinforcement Learning with Double Q-learningCode1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
Discriminator Soft Actor Critic without Extrinsic RewardsCode1
Distilling Reinforcement Learning Tricks for Video GamesCode1
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion PoliciesCode1
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value RegularizationCode1
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological ModelsCode1
Uncertainty Weighted Actor-Critic for Offline Reinforcement LearningCode1
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