Author Archive SaiGayatri Vadali

BySaiGayatri Vadali

Advantage function in Deep Reinforcement learning

Deep reinforcement learning involves building a deep learning model which enables function approximation between the input features and future discounted rewards values also called Q values. We have seen how we can effectively get these q values and create a map consisting of input features and corresponding set of q values in this article.

This map of input features and all possible q values at a given state enables the Reinforcement learning agent get an overall picture of environment which further helps the agent in choosing the optimal path.

Read the rest of the article at Mindboard’s Medium channel.

BySaiGayatri Vadali

Q Matrix Update to train Deep Recurrent Q Networks More Effectively

Deep Recurrent Q network, as discussed in previous article, can be very helpful in building smart agents that remember their learning from distant past. This feature makes a Deep Recurrent Q network a valuable function approximator in building AI agents for Deep Reinforcement Learning.

Read the rest of the article at Mindboard’s Medium channel.

BySaiGayatri Vadali

Getting started with Data Science in 30 days using R programming

This series of posts is an attempt to help those who are in this pursuit and introduce them to the world of Data using the most widely popular language – R. Here is a collection of the first 12 articles.