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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
In this paper, we apply machine learning to create bots for Halite III, @twosigma's annual A.I. competition. We develop one classifier using Support Vector Machine with Supervised Learning, and one using a Deep Neural Network with Reinforcement Learning
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<p align="center">
<img src="https://github.com/lnmangione/Halite-III/blob/master/graphics/halite2p.gif">
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<p align="center">
<text>A 2 player game of Halite III</text>
</p>
# Mastering Halite with Reinforcement Learning
## Abstract
In this paper, we apply machine learning to create computerized bots for Halite III, a resource-management game. We focus on a single-player, single-ship game, and develop two classifiers: one using Support Vector Machine with Supervised Learning, and one using a Deep Neural Network with Reinforcement Learning. We evaluate the performance of each of these bots against two benchmark bots, one simple rule-based bot and one genetically-tuned rule-based bot. We find that the machine learning bots successfully learn gameplay, and performing at a level between the two benchmarks.
## 1 Introduction
Halite is an annual open source artificial intelligence challenge, created by Two Sigma. This year’s competition, Halite III, is a resource management game in which players must program a bot that builds and commands ships to explore the game map and collect halite, an energy resource that can be found scattered across the map. Ships use halite as an energy resource, and the player with the most halite at the end of the game is the winner.
### 1.1 Game Rules
Each player begins a game of Halite III with a shipyard, where ships spawn and halite is stored. Players spend halite to build a ship, move a ship, and to convert a ship to a dropoff. Players can interact by seeking inspiring competition, or by colliding ships to send both to the bottom of the sea.
Players each start the game with 5,000 stored halite, a shipyard, and knowledge of the game map. Each game is played in groups of two or four on a two dimensional map with a unique symmetric pattern of halite.
Ships can make one action per turn: they can move one unit in any cardinal direction, collect halite from the sea in their current position, or convert into dropoffs. When a ship is over a friendly shipyard or dropoff, it automatically deposits its halite cargo increasing the player's collected halite.
Each turn, the game engine sends the players the positions of all ships and dropoffs, and an updated game map. Players have up to two seconds to issue their commands for the turn. The game engine will parse and execute the commands, calculating each player’s resulting halite score and resolving all movement. The game continues for 400 to 500 turns, depending on the game map size. The winner of the game is the player who has the most collected halite at the end.
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<img width="460" src="https://github.com/lnmangione/Halite-III/blob/master/graphics/fig1.png">
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<text> Figure 1: The Halite III Game Board </text>
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An example of a particular instance of a game state for Halite is shown in Figure 1. For more information about the rules and recordings of games of Halite III, visit the website, halite.io.
### 1.2 Importance of this topic
This rapid growth in how machines learn to play games was in no small part due to the discovery and use of new methods and algorithms along the way. One such algorithm is called minimax which works to minimize the maximum potential chances of loss. This algorithm is used in chess engines and can be applied to general decision making when there exists an element of uncertainty. The development of algorithms like this one have enhanced our ability to solve problems (Ontanon, 2012). This is why learning games is important.
## 2 Problem and Approach
### 2.1 Problem Statement
Our objective is to create a machine learning bot for Halite III that optimizes single-player resource gathering on a set map size. The decisions this bot will make per turn of the game will be with regards to ship navigation. Afterward tackling this problem, we can see how the bot generalizes in a multiplayer setting or on differently sized maps; however, this is not the Excerpt of 22,430 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:154f4912ee394efe, llm:description: applies machine learning to create bots for Halite III; develops SVM classifier (supervised) and deep neural network with reinforcement learning.
matched fp:154f4912ee394efe, llm:description: applies machine learning to create bots for Halite III; develops SVM classifier (supervised) and deep neural network with reinforcement learning.
matched fp:154f4912ee394efe, llm:description: applies machine learning to create bots for Halite III; develops SVM classifier (supervised) and deep neural network with reinforcement learning.