# Monte Carlo

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- Model Free Reinforcement learning algorithms (Monte Carlo, SARSA, Q-learning) | Madhu Sanjeevi (Mady) - Medium
- Google DeepMind AlphaGo Zero
- An Introduction to Monte Carlo Techniques in Artificial Intelligence | Todd W. Neller
- Cross-Validation
- Bayes
- Reinforcement Learning (RL)
- Monte Carlo (MC) Method - Model Free Reinforcement Learning
- Markov Decision Process (MDP)
- State-Action-Reward-State-Action (SARSA)
- Q Learning
- Deep Reinforcement Learning (DRL) DeepRL
- Distributed Deep Reinforcement Learning (DDRL)
- Evolutionary Computation / Genetic Algorithms
- Actor Critic
- Hierarchical Reinforcement Learning (HRL)

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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