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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
Dropout Q-Functions for Doubly Efficient Reinforcement LearningCode1
GAIL-PT: A Generic Intelligent Penetration Testing Framework with Generative Adversarial Imitation LearningCode1
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement LearningCode1
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
Multi-Agent Reinforcement Learning via Distributed MPC as a Function ApproximatorCode1
A Recipe for Unbounded Data Augmentation in Visual Reinforcement LearningCode1
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological ModelsCode1
Towards Universal and Black-Box Query-Response Only Attack on LLMs with QROACode1
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning TechniquesCode1
Gradient Temporal-Difference Learning with Regularized CorrectionsCode1
A Stochastic Game Framework for Efficient Energy Management in Microgrid NetworksCode1
Addressing Function Approximation Error in Actor-Critic MethodsCode1
HASCO: Towards Agile HArdware and Software CO-design for Tensor ComputationCode1
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
IQ-Learn: Inverse soft-Q Learning for ImitationCode1
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
Learning the Markov Decision Process in the Sparse Gaussian EliminationCode1
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19Code1
Boosting Continuous Control with Consistency PolicyCode1
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay BufferCode1
Uncertainty Weighted Actor-Critic for Offline Reinforcement LearningCode1
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