Game ai by example github

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This the code for 'Build a game AI' on Youtube. It will run SpaceInvaders-v0 by default but you can use other game names as well. Add --display true to the above command line argument if you'd like to see the game while it trains.

Credit for the vast majority of code here goes to Kee Hyun Won. I've merely created a wrapper to get people started.

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Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit ac1 May 23, Add --display true to the above command line argument if you'd like to see the game while it trains Credits Credit for the vast majority of code here goes to Kee Hyun Won.

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Skip to content. Instantly share code, notes, and snippets. Code Revisions 8 Stars 10 Forks 5. Embed What would you like to do? Embed Embed this gist in your website. Share Copy sharable link for this gist.

Learn more about clone URLs. Download ZIP. Simple Python implementation of the classic Tic Tac Toe game. Goal : Unbeatable AI.

Objective is to find a winning move, a blocking move or an equalizer move. Bot must always win ''' check if bot can win in the next move for i in range 0len self.

Reinforcement Learning - A Simple Python Example and a Step Closer to AI with Assisted Q-Learning

What is your name? This comment has been minimized. Sign in to view. Copy link Quote reply. Bot loses to 9,7,3,6. I think it should prefer center to corners in order to draw. Play and go first, go 1, then 2, then 5, then 9 Try it. Easy win every time.

game ai by example github

Plz fix. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Tic Tac Toe game in Python. Date: Tested with Python 2.

TO RUN:. This class holds the user interaction, and game logic". Bot must always win. If the middle is free, take it. Please try again: ".Extended final report: Download PDF. Gomoku is an abstract strategy board game.

Also called Gobang or Five in a Row, it is traditionally played with Go pieces black and white stones on a go board with 19x19 15x15 intersections. This game is known in several countries under different names. Black plays first if white just lost in the last game, and players alternate in placing a stone of their color on an empty intersection. The winner is the first player to get an unbroken row of five stones horizontally, vertically, or diagonally.

Our task is to make a Gomoku game AI which can learn the rules of Gomoku by playing games with itself. It is similar with the AlphaGo machine which is a really popular and amazing application. This kind of machine learning algorithm has a wide application scenario. That is the reason why AlphaGo team and such kind of teams have fast development recently.

Before our implementing any machine learning techniques to train a computer to play Gomoku, we need to create an interface which would allow us to simulate a game of Gomoku. This game involves two kinds of chess, player, AI, rules and so on. We implement game class, AI class, player class, gomoku class with Python by ourselves, without using any existing package.

In the game interface part, we made a very useful and interactive design. Firstly, users can choose to play with the AI which is trained before or to play with another player. The first mode allows the AI to play with itself before to train the model and the second mode tends to enhance the interactive function which allows users to play Gomoku with friends. Then, users can set their specific Gomoku rules, including the size of the chess board from 3x3 to 10x10 and the number of chess to win more than 2.

There are three kinds of strategies: attack, defend and neutral. There is a grading system to help AI make decision which kind of strategy it will take in the next step. This system is based on judging the situation, the grades of AI and player and which kind of strategy leads to a higher score.

In the AI part, we use heuristic and reinforcement learning techniques to train the model. After researching reinforcement learning techniques, we decided to use Q-learning to train our simulation player with how to play Gomoku because it learns the state-action value function and the exploration policy.

15 Python Projects in Under 15 Minutes (Code Included)

It is also an off-policy method an off-policy learner learns the value of the optimal policy independently of the agent's actionswhich gathers information from partial random moves. Comparing to the traditional Q-learning method, we combine it with the heuristic method.

This kind of combination can speed up the learning process since it simplifies the gradient descent learning process. We set five intelligence coefficients to use as alpha parameters and gamma parameters of the heuristic algorithm in three strategies attack, defend, neutral.

At the beginning of the training process, we initialize five coefficients to build up the original model and then make some changes in these five parameters to make a new mode.Most code examples will be in pseudo-code, so no specific programming language knowledge should be required. Game AI is mostly focused on which actions an entity should take, based on the current conditions. In each case it is a thing that needs to observe its surroundings, make decisions based on that, and act upon them.

For example, autonomous cars must take images of the road ahead, combine them with other data such as radar and LIDAR, and attempt to interpret what they see. The AI has the relatively simple task of deciding which direction to move the paddle. If we wanted to write AI to control the paddle, there is an intuitive and easy solution — simply try and position the paddle below the ball at all times.

By the time the ball reaches the paddle, the paddle is ideally already in place and can return the ball. Providing that the paddle can move at least as fast as the ball does, this should be a perfect algorithm for an AI player of Pong.

But it is there. Each node is one of two types:. At first glance, it might not be obvious what the benefit is here, because the decision tree is obviously doing the same job as the if-statements in the previous section.

But there is a very generic system here, where each decision has precisely 1 condition and 2 possible outcomes, which allows a developer to build up the AI from data that represents the decisions in the tree, avoiding hardcoding it.

You still need to hard-code the conditions and the actions, but now you can imagine a more complex game where you add extra decisions and actions and can tweak the whole AI just by editing the text file with the tree definition in. You could hand the file over to a game designer who can tweak the behaviour without needing to recompile the game and change the code — providing you have provided useful conditions and actions in the code already.

Where decision trees can be really powerful is when they can be constructed automatically based on a large set of examples e. This makes them an effective and highly-performant tool to classify situations based on the input data, but this is beyond the scope of a simple designer-authored system for having agents choose actions. Earlier we had a decision tree system that made use of pre-authored conditions and actions. The person designing the AI could arrange the tree however they wanted, but they had to rely on the programmer having already provided all the necessary conditions and actions they needed.

What if we could give the designer better tools which allowed them to create some of their own conditions, and maybe even some of their own actions? The decision tree data might end up looking more like this:. This is the same as above, but the decisions have their own code in them, looking a bit like the conditional part of an if-statement.

This can be done by embedding a scripting languagelike Lua or Angelscript, which allows the developer to take objects in their game e. It is often even possible to change the script file while the game is running, allowing developers to rapidly test different AI approaches. The examples above were designed to run every frame in a simple game like Pong. Imagine a shooter game where the enemies are stationary until they detect the player, and then take different actions based on who they are — the brawlers might charge towards the player, while the snipers will stay back and aim a shot.

If not, nothing happens. As with the previous examples, you can make these associations in a data file so that they can be quickly changed without rebuilding the engine. Although simple reactive systems are very powerful, there are many situations where they are not really enough. Sometimes we want to make different decisions based on what the agent is currently doing, and representing that as a condition is unwieldy.

Sometimes there are just too many conditions to effectively represent them in a decision tree or a script. Sometimes we need to think ahead and estimate how the situation will change before deciding our next move. For these problems, we need more complex solutions.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.

You can find the Yuka documentation on the website. We have several examples on the website.

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Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. JavaScript library for developing Game AI. JavaScript Other. JavaScript Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Mugen87 Update package. Latest commit fc7e Apr 9, Autonomous Agent Design: Yuka provides a basic game entity concept and classes for state-driven and goal-driven agent design.

Steering: Use the build-in vehicle model and steering behaviors in order to develop moving game entities. Navigation: Graph classes, search algorithms and a navigation mesh implementation enables advanced path finding.

Perception: Create game entities with a short-term memory and a vision component. Trigger: Use triggers to generate dynamic actions in your game. Fuzzy Logic: Make your game entities smarter with Yuka's fuzzy inference system. Yuka is a standalone library and independent of a particular 3D engine.

game ai by example github

Documentation You can find the Yuka documentation on the website. Examples We have several examples on the website.

You signed in with another tab or window. Reload to refresh your session.Programmed over 30 years ago. So I'm once again a Newby. Can't run code as it looks like it's looking for "Curses". Can anyone advise as to where I can download "Curses"?

game ai by example github

Many Thanks. I cannot run it my computer It show some error. I can run run any program using curse module. Help me plz. How do you get the curses thing?

I don't understand how adding channels or repositories or whatever its called works. I have Anaconda and i'm on windows. Thanks for sharing! I made a new game using your code as a start.

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Anyone get this??? File "snake. Hope this will help. Maybe something is wrong with the curses module. I'm not sure. Hope that helps!

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UPDATE: I have written multiple questions trying to figure out each line of code, but then keep deleting the question because since I don't give up I eventually figure it out. My Google-fuu is strong. I had an issue at line 29, within the "while key!Practical walkthroughs on machine learning, data exploration and finding insight. Machine learning is assumed to be either supervised or unsupervised but a recent new-comer broke the status-quo - reinforcement learning.

Supervised and unsupervised approaches require data to model, not reinforcement learning! Thanks Mic for keeping it simple! We create a points-list map that represents each direction our bot can take.

Using this format allows us to easily create complex graphs but also easily visualize everything with networkx graphs.

The extra added points and false paths are the obstacles the bot will have to contend with.

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If you look at the top image, we can weave a story into this search - our bot is looking for honey, it is trying to find the hive and avoid the factory the story-line will make sense in the second half of the article.

We then create the rewards graph - this is the matrix version of our list of points map. We initialize the matrix to be the height and width of our points list 8 in this example and initialize all values to -1 :. To read the above matrix, the y-axis is the state or where your bot is currently located, and the x-axis is your possible next actions.

We then build our Q-learning matrix which will hold all the lessons learned from our bot. The Q-learning model uses a transitional rule formula and gamma is the learning parameter see Deep Q Learning for Video Games - The Math of Intelligence 9 for more details. Hi there, this is Manuel Amunategui- if you're enjoying the content, don't forget to signup for my newsletter:. What if our bot could record those environmental factors and turn them into actionable insight?

Whenever the bot finds smoke it can turn around immediately instead of continuing to the factory, whenever it finds bees, it can stick around and assume the hive it close. We assign node 2 as having bees and nodes 4,5,6 as having smoke. The bot needs to do another run like we just did, but this time it needs to collect environmental factors. Here is the new update function with the capability of updating the Q-learning scores when if finds either bees or smoke.

The environmental matrices show how many bees and smoke the bot found during its journey while searching for the most efficient path to the hive. To make this walk-through simpler, I am assuming two things - we modeled the environmental data and found out that the bees have a positive coefficient on finding hives, and smokea negative one.

And we are going to reuse the environmental matrix already mapped out for our landscape, a more realistic approach would be to dynamically look at a new environment and assign environmental biases as they are encountered. We see that the bot converges in less tries, say around less, than our original model.

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