Reinforcement Learning
Source: Perfectial.com
Reinforcement Learning is defined as a Machine Learning method that is concerned with how software agents should take actions in an environment. Reinforcement Learning is a part of the deep learning method that helps you to maximize some portion of the cumulative reward.
This neural network learning method helps you to learn how to attain a complex objective or maximize a specific dimension over many steps.
Here are some important terms used in Reinforcement AI:
Agent: It is an assumed entity which performs actions in an environment to gain some reward.
Environment (e): A scenario that an agent has to face.
Reward (R): An immediate return given to an agent when he or she performs specific action or task.
State (s): State refers to the current situation returned by the environment.
Policy (π): It is a strategy which applies by the agent to decide the next action based on the current state.
Value (V): It is expected long-term return with discount, as compared to the short-term reward.
Value Function: It specifies the value of a state that is the total amount of reward. It is an agent which should be expected beginning from that state.
Model of the environment: This mimics the behavior of the environment. It helps you to make inferences to be made and also determine how the environment will behave.
Model based methods: It is a method for solving reinforcement learning problems which use model-based methods.
Q value or action value (Q): Q value is quite similar to value. The only difference between the two is that it takes an additional parameter as a current action.
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