Contact Us
A 101-103 Siddhivinayak Towers, Off S.G. Highway, Ahmedabad, Gujarat 380051

Decision making in Reinforcement Learning

by Attune World Wide / /

Reinforcement Learning is one of the most popular and interesting fields of Machine Learning. In Reinforcement Learning, a software agent makes observations and takes actions in any situation and in return it receives rewards.The objective of the agent is to learn to act in such a way that its rewards are maximized.

There are two ways in which you can make your agent learn to make decisions:

  • Policy Learning
  • Q-Learning

Policy Learning:

The algorithm used by the software agent to determine its actions is called its “Policy”.The Policy can be any algorithm you can think of.Policy Learning can be thought of as a set of directions that will tell your agent what to do.For example, a robotic vacuum cleaner whose reward is to pick all the dust, its policy can be to move forward. If you think of a policy as a function, it only has one input: the state. But knowing in advance what your policy should be isn’t easy, and requires deep knowledge of the complex function that maps state to goal.

Q-Learning:

Another way of making your agent learn is not by explicitly telling it what to do,rather giving it a framework to make its own decisions. Unlike policy learning, Q-Learning considers two inputs state and action.Q-learning will tell you the expected value of each action your agent could take.

One of the peculiar feature of Q-Learning is that it doesn’t just estimate the immediate value of taking an action in a given state but it also adds in all the future values that could be Possible. For readers familiar with corporate finance, Q-Learning is sort of like a discounted cash flow analysis – it takes all potential future value into account when determining the current value of an action (or asset). In fact, Q-Learning even uses a discount-factor to model the fact that rewards in the future are worth less than rewards now.

About Attune World Wide

What you can read next

Leave a Reply

Your email address will not be published. Required fields are marked *

Recent Posts

X