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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 951975 of 1918 papers

TitleStatusHype
Dropout Q-Functions for Doubly Efficient Reinforcement LearningCode1
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-EnsembleCode1
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes0
Cellular traffic offloading via Opportunistic Networking with Reinforcement Learning0
Learning the Markov Decision Process in the Sparse Gaussian EliminationCode1
Learning Explicit Credit Assignment for Multi-agent Joint Q-learning0
Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach0
Q-Learning Scheduler for Multi-Task Learning through the use of Histogram of Task Uncertainty0
Unifying Top-down and Bottom-up for Recurrent Visual Attention0
Value Refinement Network (VRN)0
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis0
An Attempt to Model Human Trust with Reinforcement Learning0
Robust and Data-efficient Q-learning by Composite Value-estimation0
^2-exploration for Reinforcement Learning0
Bootstrapped Hindsight Experience replay with Counterintuitive Prioritization0
Adaptive Q-learning for Interaction-Limited Reinforcement Learning0
Offline Reinforcement Learning with In-sample Q-LearningCode1
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration0
Towards Unknown-aware Deep Q-Learning0
Q-learning for real time control of heterogeneous microagent collectives0
Convergent and Efficient Deep Q Learning Algorithm0
Density Estimation for Conservative Q-Learning0
Text Generation with Efficient (Soft) Q-Learning0
Untangling Braids with Multi-agent Q-Learning0
Online Robust Reinforcement Learning with Model Uncertainty0
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