Page 161 - AI Ver 1.0 Class 10
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Reinforcement Learning
Reinforcement learning is a type of learning based approach where a machine learning algorithm enables an agent
(machine with an intelligent code) to learn in an environment to find the best possible behavior or path it should
take by performing certain actions that maximize the total cumulative reward of the agent.
In this learning approach the agent learns automatically by using hit and trial methods or through its own
experience using rewards and penalties. Each action performed by an agent gives reward for correct move and
it signals positive feedback. For wrong move it generates negative feedback and gets punishment and a penalty.
The agent explores the environment by interacting with it freely so that it is able to improve the performance by
getting the maximum positive rewards.
The best applications of reinforcement learning are self driving cars, robotics and a variety of video games available
these days.
Stage 5: Evaluation
This is a very important stage of AI Project designing and training where we properly test the system to find out
the efficiency and performance of the model.
Business Data
Understanding Understanding
Data
Preparation
Deployment
Modeling
Data
Evaluation
After the model is designed and trained then the reliability of the model is checked using Testing Data acquired at
the Data Acquisition stage.
AI Project Cycle 159

