RL | ML | ALGO TRADING | TRANSPORTATION | GAME THEORY

As the same itself suggests, almost every species including humans learn by imitating and also improvise. That’s evolution in one sentence. Similarly we can make machines mimic us and learn from a human expert. …

1. Introduction

2. Concept of Elasticity

3. Measuring Elasticity: Linear Regression

4. Example: Data & Code

5. Dynamic Pricing in Competition: Game Theory

6. Nash Equilibrium

One of the biggest challenges in e-commerce is to utilize data mining methods for the improvement of their dynamic pricing policies. Usually these products…

We introduced Nash Equilibrium solution concept in the previous blog. In this blog we will start with a continuous action example and we will discuss the applicability of Nash equilibrium in mixed strategies.

Mixed strategies are class of games where player chooses actions stochastically( i.e. …

Let’s introduce the idea of solution concept in this section. So far we stressed on representation of payoff for different combinations of unique player’s decisions in the strategic environment. These representations are useless until we apply some model to predict the decision of a given player considering the anticipated decisions…

This is an advanced theoretical blog which focusses on one of the most intriguing and complex aspect of policy gradient algorithms. The reader is assumed to have some basic understanding of policy gradient algorithms: A popular class of reinforcement learning algorithms which estimates the gradient for a function approximation. …

Thanks for responding. Its h(planks)/(2*pi). 6.6/(2*3.14) ~ 1.05 ! I will leave a comment in the code.

Hey, thanks for suggestion. I tried to type it in latex, its too much effort so I resorted to writing with stylus. Anyway I will try to redo the blog with latex eqs.