## Reinforcement Learning Glossary

In hard to keep track of all the various reinforcement learning terminologies. Often I forget the name of some of these algorithms, so I made

## Reinforcement learning: policy gradient methods

Policy gradient methods are a type of Reinforcement Learning optimization methods that works by performing gradient ascent on the parameters of a parameterized policy. This

## NLP: what is attention mechanism?

In 2022, the NLP (natural language processing) benchmarks have been dominated by transformer models, and the attention mechanism is one of the key ingredients to

## On-policy Control with Approximate Value Functions

This is a continuation fromÂ Approximate Function Methods in Reinforcement learning Episodic Sarsa with Function Approximation Reminder of what Sarsa is State, Action, Reward, State, Action

## Approximate Function Methods in Reinforcement learning

Tabular vs Function Methods In reinforcement learning, there are a few methods that are called tabular methods because they track a table of the (input,

## Statistical Reasoning of Hypothesis Testing for Beginner

For beginners I would like t give an explanation for beginner to understand the basis of hypothesis testing and I have tag various section with

## Planning with Tabular Methods in Reinforcement Learning

Tabular methods Tabular methods refer to problems in which the state and actions spaces are small enough for approximate value functions to be represented as

## Temporal Difference Control in Reinforcement Learning

Temporal Difference learning is one of the most important idea in Reinforcement Learning. We should go over the control aspect of TD to find an

## Temporal Difference Learning

Temporal Difference (TD) learning is the most novel and central idea of reinforcement learning. It combines the advantages from Dynamic Programming and Monte Carlo methods.

## Monte Carlo Methods in RL

In Reinforcement Learning, the Monte Carlo methods are a collection of methods for estimating the value functions and discovering optimal policies thru experience – sampling