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Markov Decision Process reinforcement learningMarkov Chain Process reinforcement learning
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Download Link:- • https://drive.google.com/file/d/13UQL... • Important Videos For New YouTubers: • C-Language: • #1 How to learn C Language || Charact... • Machine Learning: • Apriori Algorithm ll Generating Assoc... • Python for Beginners: • Indentation in Python ||Python Indent... • Advance Python: • OOPS In Python|Object Oriented Progra... • Markov Decision Process - MDP - Markov decision process is a way to formalize a sequential decision-making process. Thus we can formalize the reinforcement learning problem with the finite Markov decision process. There are 5 components of the Markov decision process - the agent, the environment, the states, the actions, and the rewards. The agents take action in the environment based on the current state of the environment. After every action, the environment moves t[o another state. The agent receives a reward for its action on the previous state. The goal of the agent is to maximize the total reward it receives in an episode or a specific number of steps • • our social links: • (a)www.facebook.com/DrDinesh Singh Dhakad I • (b)www.facebook.com/Dinesh Singh Dhakar II • (c)www.facebook.com/DrDinesh Singh Dhakad I • (d)www.facebook.com/DrDinesh Singh Dhakad IV • (e)www.facebook.com/DrDinesh Singh Dhakad V • (f)www.facebook.com/ProfDinesh Singh Dhakar • E-Mail iD:dineshhodcsemit/[email protected] • contact no:9399080630/7374956287
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