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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 76100 of 1918 papers

TitleStatusHype
Deep Inverse Q-learning with ConstraintsCode1
Acting in Delayed Environments with Non-Stationary Markov PoliciesCode1
Deep Recurrent Q-Learning for Partially Observable MDPsCode1
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement LearningCode1
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari GamesCode1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
Dropout Q-Functions for Doubly Efficient Reinforcement LearningCode1
Energy-based Surprise Minimization for Multi-Agent Value FactorizationCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
A Recipe for Unbounded Data Augmentation in Visual Reinforcement LearningCode1
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning TechniquesCode1
Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised LearningCode1
GAIL-PT: A Generic Intelligent Penetration Testing Framework with Generative Adversarial Imitation LearningCode1
HASCO: Towards Agile HArdware and Software CO-design for Tensor ComputationCode1
Hybrid RL: Using Both Offline and Online Data Can Make RL EfficientCode1
Image Classification by Reinforcement Learning with Two-State Q-LearningCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Learning the Markov Decision Process in the Sparse Gaussian EliminationCode1
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement LearningCode1
MAN: Multi-Action Networks LearningCode1
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay BufferCode1
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
A Stochastic Game Framework for Efficient Energy Management in Microgrid NetworksCode1
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