Difference between revisions of "Monte Carlo"

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** Monte Carlo (MC) Method - Model Free Reinforcement Learning
 
** Monte Carlo (MC) Method - Model Free Reinforcement Learning
 
** [[Markov Decision Process (MDP)]]
 
** [[Markov Decision Process (MDP)]]
 +
** [[State-Action-Reward-State-Action (SARSA)]]
 
** [[Q Learning]]
 
** [[Q Learning]]
** [[State-Action-Reward-State-Action (SARSA)]]
+
*** [[Deep Q Network (DQN)]]
 
** [[Deep Reinforcement Learning (DRL)]] DeepRL
 
** [[Deep Reinforcement Learning (DRL)]] DeepRL
 
** [[Distributed Deep Reinforcement Learning (DDRL)]]
 
** [[Distributed Deep Reinforcement Learning (DDRL)]]
** [[Deep Q Network (DQN)]]
 
 
** [[Evolutionary Computation / Genetic Algorithms]]
 
** [[Evolutionary Computation / Genetic Algorithms]]
 
** [[Actor Critic]]
 
** [[Actor Critic]]

Revision as of 06:05, 6 July 2020

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A broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. Their essential idea is using randomness to solve problems that might be deterministic in principle. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo methods are mainly used in three problem classes:[1] optimization, numerical integration, and generating draws from a probability distribution.


Monte Carlo Tree Search (MCTS)

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Monte Carlo Simulation

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