Let's say, you want to apply reinforcement learning on a simple 2D game. (ex : super mario) The easy way is of course to retrieve an abstraction of the environnment, per example using gym and/or an open-source implementation of the game. But if it's not available, I think about integrating convolutionnal layers over pixels as inputs of the RL agent. We could of course split the task in two : featurization of the images and then reinforcement learning, we probably …

So I have 4GB of turn-by-turn data for many games of a particular strategy game. It appears that most people interested in using ML to build an AI for turn-based games use reinforcement learning to build a model on the fly. Since I already have really good data, can I use supervised learning to solve this task? EDIT: I was considering using regression to assign a score to a given action based on its likelihood of eventually resulting in a …

I'm currently working on an AI playing Mandarin using Minimax. I can extract this dictionary from a game where 2 AI with different depth plays with each other: game_result = { "winner": winner, # 1, 2 for player 1 and 2 respectively "time": time, # time[1] for player 1's total time consumed, same with time[2] "score": score, # same as time "total_turn": total_turn # same as time } I'll run about 100 games. Can anyone help me with how I …

I'm working on my chess bot, and I would like to implement simple artificial intelligence for it. I'm new in it, so I'm unsure how to do it specifically on chess. I heard about Q-learning, Supervised/Unsupervised learning, Genetic algorithm, etc., which probably is not for chess. I wondered how AlphaZero was created? Probably Genetic algorithm, but chess is the game where "if A then B" might not work. It means that Q-learning is also bad for it, and so on. …

I'd like to get some practice with reinforcement learning and data analysis in the domain of video games. I'm looking for a game that logs user behaviours (e.g. UI click events, player position, item usage, other system-level information like game state etc.) either to a file or database that I can easily pull the data from Are there any games that have this level of logging built-in? I don't imagine that many commercial games will provide this type of information, …

I've never really done any practical machine learning, this is just a hobby for me. I'm trying to create a process using a neural network to learn the board game "7 Wonders." Here's how I want this experiment to be done: Take all inputs (I've calculated 1278 of them to start with). Send the inputs through a neural network with an arbitrary amount of hidden layers, randomly initialize weights, and calculate values for all possible actions to take (231 outputs …

Did IBM's DeepBlue, which defeated a human player in chess in 1997, use machine learning? I am a bit confused by this article that says that it is AI, but not ML. Whereas this article says it is AI and to some extent ML. Is it the case of an algorithm that is AI but not ML?

I know that in GANs model, there is min-max game between generator and discriminator which discriminator tries to maximize the loss function and the goal of generator is to minimize it. But why we write the loss function as min-max problem and not max-min? As I know, the loss function is not convex, so there is difference between these approaches.

I just started with ML, so this could potentially be a pretty stupid question, plz forgive me. Here's the gist of the problem: I have a list in json format like this: [ { // Final rank of the team after all the matches are done "rank": 7, // List of team members "characters": [ { "char_id": "my_char_1", "level": 2, "item_ids": [1, 2, 3] } ... ] } ] I want to predict the rank for a given "team" of …

MOBA team games have teams composed of a subset of 5 heroes from a larger set of possible heroes (say 100 heroes in the larger set) For example, a game can be between a team with heroes 1,8,43,65 and 71 and a team with heroes 3,7,23,41 and 45. What is the best way to train a model that predicts the outcome based on team compositions? For example, one option would be to have something like this: |Label | Hero1 | …

Question: Which Machine Learning approach should I choose for the AI of my computer game, where the actions of the AI do not lead to immediate rewards, but delayed rewards instead? About me: I am a complete beginner in the area of machine learning. This is my first own machine learning project. I have been part of other projects that included machine learning before, but I have never done everything from scratch and completely by myself. About the game: The …

I am building an AI using a neural network that will play Tron against a human player. The game consists of a board with fixed width and height where each player can move at any direction (except for the oposite direction he is facing). Each player's body increases per play and the first one to hit the border or any body loses. For that I am building a neural network that will be trained with data that is built with …

I am currently playing around with Keras and try to use it with various datasets. Now I have a small datasets of football game results. date, home_team, away_team, goals_home_team, goals_away_team Predicting the goals is probably too hard so I combined them into a single feature outcome (win, draw, loss). date, home_team, away_team, outcome Using random forest or decision trees, I could simply leave the teams as they are but for a NN I need some encoding. Here is my problem. …

I'm trying to build out a network layer map for a neural network to use in an NES AI. Most networks I run across on web searches are CNNs that use image data to identify things. Miles and miles and miles of papers, questions, and tutorials about image-based CNNs. Even the video game machine learning AIs are generally using rendered video frames as inputs to a CNN. I played around with SethBling's MarI/O a few years ago. But that's used …

I saw in DQN nature paper 2015 https://www.nature.com/articles/nature14236(Extended Data Table 4) some comparisons between DQN and linear Q-learning. The ratio of reward for different games are very different. For instance I saw the ratio of reward of Linear Q-learning is between 3.8 to 100 times more than DQN? What is the reason of this big difference? How we can clarify this? Is it related to complexity or sensitivity of game? I am not very familiar with complexity and properties of …

I'm reading some papers about Deep Neural Networks applied for board games, like for Go with AlphaGo, AlphaGo Zero and some other games, like Othello and Chess. Most of the works are using CNN's as a direct move predictor. I want to make some experiments using Tic Tac Toe to implement a CNN, but I want to combine Deep Learning with a competitive search algorithm as Minimax or Alpha-Beta Pruning (CNN will be a "heuristic function" or evaluation function to …

I recently tried to make a CNN which could play the game Set: https://en.wikipedia.org/wiki/Set_(game) However, I quickly learned after using my own convolutional neural network and trying to retrain Inception-V3 and AlexNet to classify pictures of three cards into "set" and "notSet", that a CNN is not useful for this task. Which machine learning algorithm would be useful for playing this game? Would an SVM work? Here are links to my last question (with some code) and the Github repo …

I read the AlphaGo Zero paper and I didn't found nothing about it in there. But I would like to know if AlphaGo Zero can adapt to the way the oponent plays (oponent profile) or something like this. Thanks!!

Let's assume that we have this magical candy machine which takes candies as input and delivers again candies as output. For any given time t, it picks a random function which is strictly increasing up to a point such as f(2) = 6 here, and then it strictly decreases. It likes to be challenging but if you become greedy it punishes you like most of the stuff in life. f(1) = 5 f(2) = 6 f(3) = 4 f(4) = …

I have a simple game I'm building for fun, just to see how well ML can work with simple data sets. Basically it's just a game where it has turns that go like this: Computer generates a random number $x$, and does not show the player. Player wagers that they can guess a number lower than $x$. Call the wager amount $w$ Player tries to guess a low number $g$. If $g \lt x$, then player gains $wg$ points. If …