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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 1351–1360 of 1918 papers

TitleStatusHype
Show Us the Way: Learning to Manage Dialog from Demonstrations—0
K-spin Hamiltonian for quantum-resolvable Markov decision processes—0
Self Punishment and Reward Backfill for Deep Q-LearningCode0
Zero-Shot Learning of Text Adventure Games with Sentence-Level Semantics—0
Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing—0
Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning—0
Reinforcement Learning for Mixed-Integer Problems Based on MPC—0
Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?—0
Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning—0
Augmented Q Imitation Learning (AQIL)Code0
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