[Review] Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method

## Detail of the paper
Title: Neural Fitted Q Iteration - First Experiences
with a Data Efficient Neural Reinforcement
Learning Method
Published Year: 2005
author: Martin Riedmiller
Link: http://ml.informatik.uni-freiburg.de/former/_media/publications/rieecml05.pdf

## Introduction
This paper introduced the novel approach which combined the classical Q-learning and multi-layer perceptron. And this is one of the origin of the further development in the domain of Deep Reinforcement Learning.
Basically this is a function approximation using multi-layer perceptron to efficiently represent Q function.
And the author firstly clarified the advantages and disadvantages arising in this approach reported by other existing research conducted by [Tes92, Lin92, Rie00].

- Advantages: Generalisation: since MLP can affect effectively globally, the model can represent the Q function efficiently compared to other local-oriented approach
- Disadvantage: Danger of the divergence in Learning: to widely cover the observations to train the model globally, this is not be assured the convergence in learning.

## Basic Concept
The basic idea underlying NFQ is the following: Instead of updating the neural
value function on-line (which leads to the problems described in the previous
section), the update is performed off-line considering an entire set of transition
experiences. Experiences are collected in triples of the form (s, a, s
) by interacting
with the (real or simulated) system1. Here, s is the original state, a is the
chosen action and s is the resulting state. The set of experiences is called the
sample set D.

## Algorithm
<img width="643" alt="Screen Shot 2018-06-11 at 18.51.37.png" src="https://qiita-image-store.s3.amazonaws.com/0/240801/bbc63940-737b-bbde-9d78-315395f1cc6c.png">

## Benchmarking
- avoidance control task - keep the system somewhere within the ’valid’ region
of state space. Pole balancing is typically defined as such a problem, where
the task is to avoid that the pole crashes or the cart hits the boundary of
the track.
- reaching a goal - the system has to reach a certain area in state space. As soon
as it gets there, the task is immediately finished. Mountaincar is typically
defined as getting the cart to a certain position up the hill.
- regulator problem - the system has to reach a certain region in state space
and has to be actively kept there by the controller. This corresponds to the
problems typically tackled with methods of classical control theory.

## Empirical Results
- Pole Balancing Task  
<img width="410" alt="Screen Shot 2018-06-11 at 19.06.44.png" src="https://qiita-image-store.s3.amazonaws.com/0/240801/6d558140-f03d-60df-5b4f-35ead341022b.png">
- The Mountain Car Benchmark  
<img width="426" alt="Screen Shot 2018-06-11 at 19.07.13.png" src="https://qiita-image-store.s3.amazonaws.com/0/240801/3061ecf5-6142-8b61-cd85-b98669593ff0.png">

## Conclusion
The author has concluded that utilising the experience reply, we can avoid the issues discussed above, and achieve the efficient training.

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