Recent Posts

Reinforcement Learning : Tabular Solution Methods

Multi-armed Bandits The multi-armed bandit problem serves as an introductory concept in reinforcement learning, much like how the house pricing problem acts as a starting point in machine learning. In the K-armed bandit problem, an agent is presented with a set of ‘k’ actions to choose from, and the agent’s decision results in a corresponding reward. Value of an action is the expected or mean reward given when that action is selected.

Reinforcement Learning : Fundamentals

Reinforcement learning is the process of discovering what to do in order to maximise a numerical reward signal. A reinforcement learning system’s primary sub-components are policy, reward signal, value function, agent, and environment.

Linux Basics

These are some basics linux terminal commands which are of great use for developers and linux lovers.