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Protected: Policy Gradient Methods for Reinforcement Learning with Function Approximation

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Author: Z Pei on February 17, 2019

Categories: AI, Function Approximation, Policy Gradient Methods, Reinforcement Learning, RL, Stationary Distribution

Tags: AI, Function Approximation, Policy Gradient Methods, Reinforcement Learning, RL, Stationary Distribution

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Protected: Policy Gradient Methods for Reinforcement Learning with Function Approximation

Last update on June 21, 2019

Published February 17, 2019

Author: Z Pei

Categories: AI, Function Approximation, Policy Gradient Methods, Reinforcement Learning, RL, Stationary Distribution

Tags: AI, Function Approximation, Policy Gradient Methods, Reinforcement Learning, RL, Stationary Distribution

Related Posts

Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
Solving H-horizon, Stationary Markov Decision Problems In Time Proportional To Log(H)
Randomized Linear Programming Solves the Discounted Markov Decision Problem In Nearly-Linear (Sometimes Sublinear) Run Time
KL Divergence
The Asymptotic Convergence-Rate of Q-learning

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