In my last article, I have discussed on reinforcement learning briefly. Today let’s talk about some algorithms in reinforcement learning.
To achieve the optimal policy, which is used in reinforcement leaning, there have algorithms in reinforcement learning. In this article I am going to explain some main reinforcement learning algorithms briefly.
To learn the optimal action in unknown environment, Q-learning is the simple algorithm in reinforcement learning. Without having a model of an environment, it can learn the optimal and long-term action. This is off-policy. And there have two policies called target policy and behavior policy. Tabular methods give correct policies and functions in tables. In Q-learning, to find optimal action value function, behavior policy can be achieved using policy iteration. …
In machine learning, there have main three areas. Supervised learning, unsupervised learning and reinforcement leaning. Reinforcement learning gives solutions for various kind of planning and control problems. Policy, model of environment, reward signal and value function are major components of reinforcement leaning.
Reinforcement learning can consider as a part of an artificial intelligence. It has become apparent in artificial intelligent systems and fixing sequential decision-making problems. And it was able to pass human level in various fields. Reinforcement learning use different actions and experiment in many successes and failures to interact with an unknown environment. …
Here are some commands in npm:
When someone says that “You look young for your age” that implies, one person can inhabit to two ages at once. They are called as biological age and chronological age.
Chronological age means the the actual amount of time person has been alive. In other word that indicates the number of candles that we blow out every year. Biological age refers that how old a person seems. That means also the physiological age of a person.
When growing older, hormone levels of person’s can be increase and also decrease. Decreasing of some hormones make us more older. …
Brief Introduction to TensorFlow
What is TensorFlow?
TensorFlow is a free and open-source software library. It is a symbolic math library. Basically, is can used to developments in machine learning. TensorFlow was developed by the Google Brain team. And it was written using Python, C++, CUDA. It is supported to Linux, macOS, Windows and Android.
What are Top Uses of TensorFlow?
To do developments in large-scale neural network applications, TensorFlow can be used. Mainly, it can be used for Classification, Perception, Understanding, Discovering and Creation. Here are some areas that can be used TensorFlow for developments.
· Image Recognition
The object recognition algorithms in TensorFlow can use to identify objects in larger images. Usually, this is used in engineering applications, to 3D space construction from 2D images. …