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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 1221–1230 of 1918 papers

TitleStatusHype
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space—0
Near-Optimal Regret Bounds for Model-Free RL in Non-Stationary Episodic MDPs—0
A New Approach for Tactical Decision Making in Lane Changing: Sample Efficient Deep Q Learning with a Safety Feedback Reward—0
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based GamesCode0
Is Q-Learning Provably Efficient? An Extended Analysis—0
Hidden Incentives for Auto-Induced Distributional Shift—0
Energy-based Surprise Minimization for Multi-Agent Value FactorizationCode1
Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing—0
Deep Reinforcement Learning for Option Replication and Hedging—0
AoI Minimization in Status Update Control with Energy Harvesting Sensors—0
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