Overview: Generative Adversarial Networks – When Deep Learning Meets Game Theory

Before going into the main topic of this article, which is about a new neural network model architecture called Generative Adversarial Networks (GANs), we need to illustrate some definitions and models in Machine Learning and Artificial Intelligence in general.

Discriminative Models: Models that predict a hidden observation (called class) given some evidence (called features). In other words, we have some features and observations about an entity, and we want to predict its class, category or label. You can imagine the model as a function that has features as input and produces an output. The criteria that is used to produce the output depends on the model architecture and nature.

So, the discriminative model could be described in mathematical formula by f(x1, x2, x3, …, xn) = y, where n is the number of features, and the target of the function is to get the conditional probability P(y|x1, x2, x3, …, xn).

Support Vector Machines (SVMs) and Feedforward Neural Networks (FNNs) are examples of discriminative models that are used for classification.

Generative Models: Given some features, the models target is to learn how these features are produced, it tries to learn the distribution of the features. Assume that we have features x1, x2, x3, …, xn where n is the number of features, the model targets to learn the joint probability distribution of the features with the classes.

We can formulate this in mathematics by the joint probability P(x1, x2, x3, …, xn, y). After learning this distribution, we can estimate the conditional probability of the discriminative models to get the probability of the class given these features with their distribution.

Restricted Boltzmann Machines (RBMs) and Hidden Markov Models (HMMs) are examples of generative models. Note that, Vanilla Auto-encoders (AEs) aren’t considered to be a generative model, all what they do is just reconstruction of the features, on the other hand, Variational Auto-encoders (VAEs) belongs to generative models family.

Nash Equilibrium: A conceptual term that is used in Game Theory to describe a game situation in which the game players are satisfied by the decision he/she makes after revealing the other players strategies, and each player has no intention to change the strategy after knowing the other strategies as they didn’t effect on the strategy he/she used to win the game.

For example, assume we have a game in which each player has 2 choices to choose between them, and the 2 choices are known that are right and have the same effect regarding to the game points or rewards. The first player strategy maybe to choose the first choice, while the other player’s choice is the second one. After revealing that, each player is satisfied by the strategy he/she took because the other’s choice hasn’t effect badly to him/her.

Minimax: An algorithm that belongs to Game Theory and Statistics. The algorithm is used in games in which the game participants are 2 players, and each player tries to win the game by minimizing the worst case that is provided by the other player move, in other words, the player Minimize the Maximum move of the other player.

You can imagine the game of Chess, in which each player tries to win by making the best available move while the other player tries to minimize this move which is considered to be the best move by his side. Minimax is commonly used when making an AI-bot agent in Chess, Tic-tak-toc and Connect-4 games, you can generalize in the decision-making rule-based games.

Generative Adversarial Networks (GANs)

GANs consists of 2 models, a discriminative model (D) and a generative model (G). These models are participants on the training phase which looks like a game between them, and each model tries to better than the other.

The target of the generative model is to generate samples that are considered to be fake and are supposed to have the same distribution of the original data samples, on the other hand, the discriminative’s target is to enhance itself to be able to recognize the real samples among the fake samples generated by the generative model.

It looks like a game, in which each player (model) tries to be better than the other, the generative model tries to generate samples that deceives and tricks the discriminative model, while the discriminative model tries to get better in recognizing the real data and avoid the fake samples. It is as mentioned before, it is the same idea of the Minimax algorithm, in which each player targets to fail the other and minimize the supposed loss.

This game continues till we get a state, in which each model becomes an expert on what it is doing, the generative model increases its ability to get the actual data distribution and produces data like it, and the discriminative becomes expert in identifying the real samples, which increases the system’s classification task. In such case, we know that it reached that in which each model satisfied by its output (strategy), which is called Nash Equilibrium in Game Theory.

During the training phase, the loss function, that calculates the error, is used to update the 2 models parameters (learning the weights), also, the model can’t change the other’s parameters, the parameters are locally updated in each model using the global error.

This was an overview of this new arising model. I am still learning it and looking forward to using it in many applications, specifically in Natural Language Processing field.


– http://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html
– https://en.wikipedia.org/wiki/Generative_adversarial_networks
– https://en.wikipedia.org/wiki/Minimax
– https://en.wikipedia.org/wiki/Nash_equilibrium
Artificial Intelligence: A Modern Approach book by Stuart Russell and Peter Norvig

Introduction to Reinforcement Learning

Hey, what’s up guys?

Today is the beginning of a series about an interesting field of science and new for me. The series is about Reinforcement Learning.

The reason why I have started to learn this topic is that I have known about supervised and unsupervised learning in through Andrew NG Machine Learning course, I thought that these two techniques are the only available approaches for machine learning (regardless Evolutionary and Swarm methods), but later I found that there are other approaches like Semi-supervised and Reinforcement Learning, so, I decided to get through Reinforcement Learning to know more about it and its algorithms.

In this series I explain what I learnt about Reinforcement Learning and mention the resources I use during my study provided with my code implementation. The main reasons for these blogging is that I didn’t find many blogs talking about this field, it will motivate me to finish what I want to learn and also to memorize what I studied, you can consider them as documentations for each chapter.

Now, let’s get started.

Chapter 1: Introduction to Reinforcement Learning

Before we get into our topic, we need to talk about Supervised and Unsupervised Learning and mention the differences between each of these 2 techniques as they are very important to define the meaning of Reinforcement learning.

In supervised learning, we have a training data –could be images or numerical values– , it consists of some of training examples that were obtained before, training data is used to train the classifier which could be any known classifier such as SVM, KNN ..Etc. or we may use Neural Nets. The classifier uses this labeled data to help on identifying and classifying new data –known as test data–, the main factor for classification is extracting the object’s features –known as feature vector– that are considered to be the descriptors of the object.

In unsupervised learning, you don’t have a training data, you are given set of unlabeled data, and the task is to cluster these data into separate classes, clustering is based on the features of the given items, the well-known algorithm for clustering is K-means algorithms and its variants, also, the famous Hidden Markov Models (HMM) are used too in unsupervised learning.

On the other hand, reinforcement learning is simply learning what to do to get the best benefit in the current situation, the agent has the ability to learn from the current environment after several trials. Back to supervised learning, you will find that training data is a must for classification, and that isn’t practically in some of interactive problem in which the agent should decide what to do in the run time on the current environment. The solution is that the agent should has the ability from its experience instead of using training data and that is introduced in reinforcement learning.

As in supervised and unsupervised learning, reinforcement learning has some basic characteristics that defines and describes its process, we can conclude them in 3 points:-

  • Trials: In which the agent uses many trials in the same environment to obtain and predict the opponent’s behavior (like in chess game)
  • Error: After each situation or state in the environment, the agent calculates the error in its search to minimize it in a similar situation
  • Reward: The benefits which the agent will obtain after perform the action

Also, the agent which uses reinforcement learning has two important choices when choosing its action or prediction:-

  • Exploitation: The ability to use a previous successful experience in the current situation
  • Exploration: The ability to explore other solutions or paths which could lead to better reward

When we build an agent which uses reinforcement learning to perform some actions, we need at first to define some elements that the agent uses to learn in the environment. Scientists identified main 4 elements for the learning system:-

  • Policy
  • Reward Function
  • Value Function
  • Model (optional)

We will talk about each of these elements, and to fully understand these elements we will use Tic-Tac-Toe as an example.

Policy defines the agent’s behavior and attitude in the environment in a specific state. In Tic-Tac-Toe, the agent should play ‘X’ in each of its turn, this is the policy of the agent. After consider this policy, an estimation should be done to estimate the winning probability for each move.

Reward Function defines the benefit or desirability of the next state –only– after agent’s move, the higher of the reward, the better solution. In Tic-Tac-Toe, When the agent plays ‘X’, it estimates the reward which will be obtained after being in the resultant state, on other words, higher reward means a good move, and lower reward indicates a bad move.

Value Function defines the total rewards in the long run of all the states, and that is different from the Reward Function which only focuses in the next state only. We can obtain from that the Value Function is more important that the Reward Function.

Model is an optional element when we build an agent and only appears in Planning that is used in games, the model predicts the next state of the current environment given the current state and the action which the agent will perform. This simulation or prediction effects on the decision making process of the agent.

Note: A good agent may use a lower reward in a specific state if this state will lead it to be in other higher Rewarded state, and that could give a better Value Function.

The following shows a diagram of an agent in Tic-Tac-Toe uses reinforcement learning to perform its next moves


  • The opponent started the game in point ‘a’, turning the game state to be in state ‘b’
  • In state ‘b’ the agent checked all possible states which is presented as dashed lines, then selected –the policy– one of them presented as state ‘c’
  • The opponent played its move, turning the game to state ‘d’
  • In state ‘d’, the agent made an exploratory move that leaded it to be in state ‘e*’, but the agent found that it wasn’t a good state which won’t resultant for learning
  • The agent backtracked and chose another state ‘e’
  • The curved lines donates a backup for the state, the reason for that is to get better winning estimation after each move, so, the backup is an option for the agent


In the next chapter, we will talk about one of reinforcement learning application.