38 research outputs found
Learning macromanagement in starcraft from replays using deep learning
The real-time strategy game StarCraft has proven to be a challenging
environment for artificial intelligence techniques, and as a result, current
state-of-the-art solutions consist of numerous hand-crafted modules. In this
paper, we show how macromanagement decisions in StarCraft can be learned
directly from game replays using deep learning. Neural networks are trained on
789,571 state-action pairs extracted from 2,005 replays of highly skilled
players, achieving top-1 and top-3 error rates of 54.6% and 22.9% in predicting
the next build action. By integrating the trained network into UAlbertaBot, an
open source StarCraft bot, the system can significantly outperform the game's
built-in Terran bot, and play competitively against UAlbertaBot with a fixed
rush strategy. To our knowledge, this is the first time macromanagement tasks
are learned directly from replays in StarCraft. While the best hand-crafted
strategies are still the state-of-the-art, the deep network approach is able to
express a wide range of different strategies and thus improving the network's
performance further with deep reinforcement learning is an immediately
promising avenue for future research. Ultimately this approach could lead to
strong StarCraft bots that are less reliant on hard-coded strategies.Comment: 8 pages, to appear in the proceedings of the IEEE Conference on
Computational Intelligence and Games (CIG 2017
Imitative learning for designing intelligent agents for video games
Over the past decades, video games have become increasingly popular and complex. Virtual worlds have gone a long way since the first arcades and so have the artificial intelligence (AI) techniques used to control agents in these growing environments. Tasks such as world exploration, constrained pathfinding or team tactics and coordination just to name a few are now default requirements for contemporary video games. However, despite its recent advances, video game AI still lacks the ability to learn. In this work, we attempt to break the barrier between video game AI and machine learning and propose a generic method allowing real-time strategy (RTS) agents to learn production strategies from a set of recorded games using supervised learning. We test this imitative learning approach on the popular RTS title StarCraft II and successfully teach a Terran agent facing a Protoss opponent new production strategies
StarCraft Bots and Competitions
International audienceDefinition Real-Time Strategy (RTS) games is a sub-genre of strategy games where players need to build an economy (gathering resources and building a base) and military power (training units and researching technologies) in order to defeat their opponents (destroying their army and base). Artificial Intelligence (AI) problems related to RTS games deal with the behavior of an artificial player. Since 2010, many international competitions have been organized to match AIs, or bots, playing to the RTS game StarCraft. This chapter presents a review of all major international competitions from 2010 until 2015, and details some competing StarCraft bots. State of the Art Bots for StarCraft Thanks to the recent organization of international game AI competitions fo-cused around the popular StarCraft game, several groups have been working on integrating many of the techniques developed for RTS game AI into complete "bots", capable of playing complete StarCraft games. In this chapter we will overview some of the currently available top bots, and their results of recent competitions
Overview of deep reinforcement learning in partially observable multi-agent environment of competitive online video games
In the late 2010’s classical games of Go, Chess and Shogi have been considered ’solved’ by deep
reinforcement learning AI agents. Competitive online video games may offer a new, more challenging environment for deep reinforcement learning and serve as a stepping stone in a path to real
world applications. This thesis aims to give a short introduction to the concepts of reinforcement
learning, deep networks and deep reinforcement learning. Then the thesis proceeds to look into few
popular competitive online video games and to the general problems of AI development in these
types of games. Deep reinforcement learning algorithms, techniques and architectures used in the
development of highly competitive AI agents in Starcraft 2, Dota 2 and Quake 3 are overviewed.
Finally, the results are looked into and discussed