Difference between revisions of "Monte Carlo"
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* [http://modelai.gettysburg.edu/2014/mc1/index.html An Introduction to Monte Carlo Techniques in Artificial Intelligence | Todd W. Neller] | * [http://modelai.gettysburg.edu/2014/mc1/index.html An Introduction to Monte Carlo Techniques in Artificial Intelligence | Todd W. Neller] | ||
* [[Reinforcement Learning (RL)]] | * [[Reinforcement Learning (RL)]] | ||
| − | + | ** [[Markov Decision Process (MDP)]] | |
| + | ** [[Q Learning]] | ||
| + | ** [[State-Action-Reward-State-Action (SARSA)]] | ||
| + | ** [[Deep Reinforcement Learning (DRL)]] DeepRL | ||
| + | *** [[IMPALA (Importance Weighted Actor-Learner Architecture)]] | ||
| + | ** [[Distributed Deep Reinforcement Learning (DDRL)]] | ||
| + | ** [[Deep Q Network (DQN)]] | ||
| + | ** [[Evolutionary Computation / Genetic Algorithms]] | ||
| + | ** [[Asynchronous Advantage Actor Critic (A3C)]] | ||
| + | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
| + | *** [[HIerarchical Reinforcement learning with Off-policy correction(HIRO)]] | ||
| + | ** [[MERLIN]] | ||
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. | 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. | ||
Revision as of 15:03, 1 September 2019
- 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
- Reinforcement Learning (RL)
- Markov Decision Process (MDP)
- Q Learning
- State-Action-Reward-State-Action (SARSA)
- Deep Reinforcement Learning (DRL) DeepRL
- Distributed Deep Reinforcement Learning (DDRL)
- Deep Q Network (DQN)
- Evolutionary Computation / Genetic Algorithms
- Asynchronous Advantage Actor Critic (A3C)
- Hierarchical Reinforcement Learning (HRL)
- MERLIN
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
Youtube search... ...Google search
Monte Carlo Simulation
Youtube search... ...Google search
