Difference between revisions of "Markov Decision Process (MDP)"
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| + | [http://www.youtube.com/results?search_query=Markov+Decision+Process+MDP Youtube search...] | ||
| + | [http://www.google.com/search?q=Markov+Decision+Process+MDP+machine+learning+ML+artificial+intelligence ...Google search] | ||
| + | |||
| + | * [[Markov Model (Chain, Discrete Time, Continuous Time, Hidden)]] | ||
| + | |||
| + | * [[Reinforcement Learning (RL)]] | ||
| + | ** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning | ||
| + | ** [[Markov Decision Process (MDP)]] | ||
| + | ** [[State-Action-Reward-State-Action (SARSA)]] | ||
| + | ** [[Q Learning]] | ||
| + | *** [[Deep Q Network (DQN)]] | ||
| + | ** [[Deep Reinforcement Learning (DRL)]] DeepRL | ||
| + | ** [[Distributed Deep Reinforcement Learning (DDRL)]] | ||
| + | ** [[Symbiotic Intelligence]] ... [[Bio-inspired Computing]] ... [[Neuroscience]] ... [[Connecting Brains]] ... [[Nanobots#Brain Interface using AI and Nanobots|Nanobots]] ... [[Molecular Artificial Intelligence (AI)|Molecular]] ... [[Neuromorphic Computing|Neuromorphic]] ... [[Evolutionary Computation / Genetic Algorithms| Evolutionary/Genetic]] | ||
| + | ** [[Actor Critic]] | ||
| + | *** [[Asynchronous Advantage Actor Critic (A3C)]] | ||
| + | *** [[Advanced Actor Critic (A2C)]] | ||
| + | *** [[Lifelong Latent Actor-Critic (LILAC)]] | ||
| + | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
| + | |||
| + | |||
| + | http://miro.medium.com/max/1200/1*mUyxMUpzQWX4GNTd7TT4nA.gif | ||
| + | |||
| + | http://upload.wikimedia.org/wikipedia/commons/thumb/a/ad/Markov_Decision_Process.svg/600px-Markov_Decision_Process.svg.png | ||
Solutions: | Solutions: | ||
* [http://www.google.com/search?q=Dynamic+Programming+reinforcement+learning&oq=Dynamic+Programming+reinforcement+learning Dynamic Programming] | * [http://www.google.com/search?q=Dynamic+Programming+reinforcement+learning&oq=Dynamic+Programming+reinforcement+learning Dynamic Programming] | ||
| − | * [ | + | * [[Monte Carlo]] |
* [http://www.google.com/search?ei=NJMKW97aLof_zgKM8KSgBA&q=Temporal+Difference+reinforcement+learning Difference Learning] | * [http://www.google.com/search?ei=NJMKW97aLof_zgKM8KSgBA&q=Temporal+Difference+reinforcement+learning Difference Learning] | ||
| − | Used where outcomes are partly random and partly under the control of a decision maker. MDP is a discrete time stochastic control process. At each time step, the process is in some state s, and the decision maker may choose any action a that is available in state s. The process responds at the next time step by randomly moving into a new state s', and giving the decision maker a corresponding reward R_{a}(s,s')} R_a(s,s'). The probability that the process moves into its new state s' is influenced by the chosen action. | + | Used where outcomes are partly random and partly under the control of a decision maker. MDP is a discrete time stochastic control process. At each time step, the process is in some state s, and the decision maker may choose any action a that is available in state s. The process responds at the next time step by randomly moving into a new state s', and giving the decision maker a corresponding reward R_{a}(s,s')} R_a(s,s'). The probability that the process moves into its new state s' is influenced by the chosen action. Helping the convergence of certain algorithms a discount rate (factor) makes an infinite sum finite. |
| − | <youtube> | + | |
| + | <youtube>my207WNoeyA</youtube> | ||
<youtube>jpmZp3eX-wI</youtube> | <youtube>jpmZp3eX-wI</youtube> | ||
<youtube>EqUfuT3CC8s</youtube> | <youtube>EqUfuT3CC8s</youtube> | ||
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<youtube>Csiiv6WGzKM</youtube> | <youtube>Csiiv6WGzKM</youtube> | ||
<youtube>tO6hTI8CXaM</youtube> | <youtube>tO6hTI8CXaM</youtube> | ||
| + | <youtube>i0o-ui1N35U</youtube> | ||
| + | <youtube>9g32v7bK3Co</youtube> | ||
| + | <youtube>PYQAI6Td2wo</youtube> | ||
| + | |||
| + | |||
| + | == (Richard) Bellman Equation == | ||
| + | * [https://towardsdatascience.com/introduction-to-reinforcement-learning-markov-decision-process-44c533ebf8da Reinforcement Learning : Markov-Decision Process (Part 1) | Ayush Singh - Towards Data Science] | ||
| + | * [http://towardsdatascience.com/reinforcement-learning-markov-decision-process-part-2-96837c936ec3 Reinforcement Learning: Bellman Equation and Optimality (Part 2) | Ayush Singh - Towards Data Science] | ||
| + | |||
| + | http://miro.medium.com/max/690/1*5PGCR0jwd15kLhRCA09R1w.gif | ||
| + | |||
| + | <youtube>14BfO5lMiuk</youtube> | ||
| + | <youtube>aNuOLwojyfg</youtube> | ||
Latest revision as of 20:27, 13 July 2023
Youtube search... ...Google search
- 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)
- Symbiotic Intelligence ... Bio-inspired Computing ... Neuroscience ... Connecting Brains ... Nanobots ... Molecular ... Neuromorphic ... Evolutionary/Genetic
- Actor Critic
- Hierarchical Reinforcement Learning (HRL)
Solutions:
Used where outcomes are partly random and partly under the control of a decision maker. MDP is a discrete time stochastic control process. At each time step, the process is in some state s, and the decision maker may choose any action a that is available in state s. The process responds at the next time step by randomly moving into a new state s', and giving the decision maker a corresponding reward R_{a}(s,s')} R_a(s,s'). The probability that the process moves into its new state s' is influenced by the chosen action. Helping the convergence of certain algorithms a discount rate (factor) makes an infinite sum finite.
(Richard) Bellman Equation
- Reinforcement Learning : Markov-Decision Process (Part 1) | Ayush Singh - Towards Data Science
- Reinforcement Learning: Bellman Equation and Optimality (Part 2) | Ayush Singh - Towards Data Science
