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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 61–70 of 1918 papers

TitleStatusHype
Residual Policy Gradient: A Reward View of KL-regularized Objective—0
Exploring Competitive and Collusive Behaviors in Algorithmic Pricing with Deep Reinforcement Learning—0
Multi-Agent Q-Learning Dynamics in Random Networks: Convergence due to Exploration and Sparsity—0
PairVDN - Pair-wise Decomposed Value FunctionsCode0
A Novel Multi-Objective Reinforcement Learning Algorithm for Pursuit-Evasion Game—0
Generative Multi-Agent Q-Learning for Policy Optimization: Decentralized Wireless Networks—0
Quantum-Inspired Reinforcement Learning in the Presence of Epistemic Ambivalence—0
Multi-Agent Inverse Q-Learning from Demonstrations—0
DO-IQS: Dynamics-Aware Offline Inverse Q-Learning for Optimal Stopping with Unknown Gain Functions—0
Navigating Intelligence: A Survey of Google OR-Tools and Machine Learning for Global Path Planning in Autonomous Vehicles—0
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