Stanford CS234 Reinforcement Learning | Winter 2019 | Lecture 10 Policy Gradient III Review

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.What is reinforcement learning examples? Summary: Reinforcement Learning is a Machine Learning method. ... Agent, State, Reward, Environment, Value function Model of the environment, Model based methods, are some important terms using in RL learning method. The example of reinforcement learning is your cat is an agent that is exposed to the environment.What is reinforcement learning theory? The reinforcement learning theory is based on Markov decision processes, in which a combination of an action and a particular state of the environment entirely determines the probability of getting a particular amount of reward as well as how the state will change.What is reinforcement learning in simple words? Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.How does reinforced learning work? Reinforcement learning is the process of running the agent through sequences of state-action pairs, observing the rewards that result, and adapting the predictions of the Q function to those rewards until it accurately predicts the best path for the agent to take. That prediction is known as a policy.