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The goal of this project is to build an RL-based algorithm that can help cab drivers maximize their profits by improving their decision-making process on the field. Taking long-term profit as the goal, a method is proposed based on reinforcement learning to optimize taxi driving strategies for profit maximization. This optimization problem is formulated as a Markov Decision Process i.e. MDP.
This file explores the working of various Gradient Descent Algorithms to reach a solution. Algorithms used are: Batch Gradient Descent, Mini Batch Gradient Descent, and Stochastic Gradient Descent
This is a Research of Various Optimization Algorithms that are used in ML and DL which is implemented on the 2 types of Dataset(Banglore_Housing & TSP)
Gradient Descent with multiple method: Univariate - Multivariate, Momentum, Batch Gradient Descent, ...