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.youtube.com/results?search_query=Markov+Decision+Process+MDP Youtube search...] | ||
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* [[Reinforcement Learning (RL)]] | * [[Reinforcement Learning (RL)]] | ||
** [[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)]] | ** [[State-Action-Reward-State-Action (SARSA)]] | ||
** [[Q Learning]] | ** [[Q Learning]] | ||
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** [[Deep Reinforcement Learning (DRL)]] DeepRL | ** [[Deep Reinforcement Learning (DRL)]] DeepRL | ||
** [[Distributed Deep Reinforcement Learning (DDRL)]] | ** [[Distributed Deep Reinforcement Learning (DDRL)]] | ||
| − | ** [[Evolutionary Computation / Genetic Algorithms]] | + | ** [[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]] | ** [[Actor Critic]] | ||
*** [[Asynchronous Advantage Actor Critic (A3C)]] | *** [[Asynchronous Advantage Actor Critic (A3C)]] | ||
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** [[Hierarchical Reinforcement Learning (HRL)]] | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
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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. | 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. | ||
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| + | == (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] | ||
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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
