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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 19111918 of 1918 papers

TitleStatusHype
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement LearningCode0
Agent Performing Autonomous Stock Trading under Good and Bad SituationsCode0
Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction ApproachCode0
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function ApproximationCode0
QLBS: Q-Learner in the Black-Scholes(-Merton) WorldsCode0
Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple ReuseCode0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
OmniEcon Nexus: Global Microeconomic Simulation EngineCode0
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