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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 951–975 of 1918 papers

TitleStatusHype
Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach—0
Dynamic operator management in meta-heuristics using reinforcement learning: an application to permutation flowshop scheduling problems—0
Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques—0
Dynamic Retail Pricing via Q-Learning -- A Reinforcement Learning Framework for Enhanced Revenue Management—0
Dynamic value alignment through preference aggregation of multiple objectives—0
Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimization—0
Attention-Enhanced Prioritized Proximal Policy Optimization for Adaptive Edge Caching—0
Edge Delayed Deep Deterministic Policy Gradient: efficient continuous control for edge scenarios—0
EduQate: Generating Adaptive Curricula through RMABs in Education Settings—0
EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning—0
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning—0
Event-Based Communication in Distributed Q-Learning—0
Efficient Drone Mobility Support Using Reinforcement Learning—0
Efficient LSTM Training with Eligibility Traces—0
Efficient Off-Policy Q-Learning for Data-Based Discrete-Time LQR Problems—0
Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery—0
Logit-Q Dynamics for Efficient Learning in Stochastic Teams—0
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning—0
Efficient Triangular Arbitrage Detection via Graph Neural Networks—0
Elastic Decision Transformer—0
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL—0
Emergence of Addictive Behaviors in Reinforcement Learning Agents—0
Emergence of cooperation under punishment: A reinforcement learning perspective—0
Empirical evaluation of a Q-Learning Algorithm for Model-free Autonomous Soaring—0
Empirically Evaluating Multiagent Learning Algorithms—0
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