In the last post, we learn how to train a simple MOBA game using Deep Reinforcement Learning. In this post, I am going to explain what we need to know before applying the same method to the Dota2. You just need to run the Dotaservice and that code together at same PC. Unlike Derk training, each headless environment of Dota2 requires more than 1GB of RAM memory. Therefore, it is better to use a separate PC for running only environment because DRL training is usually better when there are many environments.

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DOTA2 is a popular Massive Online Battle Arena (MOBA) game with millions of daily active users. Players pick from 121 heroes and play in 5v5 games. The game is known to have a steep learning curve and there is no one hero that guarantees a win. Indeed, for every hero there seems to be another that is the perfect counter to it. DOTA2 is also a constantly evolving game where items and heroes get either buffed or nerfed in order to keep the game balanced.

The ability to extract sequences of game events for high-resolution e-sport games has traditionally required access to the game's engine. This serves as a barrier to groups who don't possess this access. It is possible to apply deep learning to derive these logs from gameplay video, but it requires computational power that serves as an additional barrier. These groups would benefit from access to these logs, such as small e-sport tournament organizers who could better visualize gameplay to inform both audience and commentators. In this paper we present a combined solution to reduce the required computational resources and time to apply a convolutional neural network (CNN) to extract events from e-sport gameplay videos. This solution consists of techniques to train a CNN faster and methods to execute predictions more quickly. This expands the types of machines capable of training and running these models, which in turn extends access to extracting game logs with this approach. We evaluate the approaches in the domain of DOTA2, one of the most popular e-sports. Our results demonstrate our approach outperforms standard backpropagation baselines.

Update: Dota 2 does provide a scripting interface, enabling bots to be written in Lua. However, this limited interface does not enable bots to communicate with remote processes and save data about games played. OpenAI Five is a huge step forward for AI, but it's also really intimidating for AI researchers. Never before has there been so many open tools for building AI systems, but it also feels like the barrier to entry for academics has actually increased over recent years. I posted an open call for any interested parties to build the best StarCraft AI possible back in 2009, and it was open to anyone interested in AI. Now, it seems like you need to have access to closed APIs, massive compute power, and historic training data to make advances in AI.

Multiplayer Online Battle Arena (MOBA) is currently one of the most popular genres of digital games around the world. The domain of knowledge contained in these complicated games is large. It is hard for humans and algorithms to evaluate the real-time game situation or predict the game result. In this paper, we introduce MOBA-Slice, a time slice based evaluation framework of relative advantage between teams in MOBA games. MOBA-Slice is a quantitative evaluation method based on learning, similar to the value network of AlphaGo. It establishes a foundation for further MOBA related research including AI development. In MOBA-Slice, with an analysis of the deciding factors of MOBA game results, we design a neural network model to fit our discounted evaluation function. Then we apply MOBA-Slice to Defense of the Ancients 2 (DotA2), a typical and popular MOBA game. Experiments on a large number of match replays show that our model works well on arbitrary matches. MOBA-Slice not only has an accuracy 3.7% higher than DotA Plus Assistant at result prediction, but also supports the prediction of the remaining time of the game, and then realizes the evaluation of relative advantage between teams.

Texas A&M University-Commerce Affiliation PLAYER SKILL DECOMPOSITION IN MULTIPLAYER ONLINE BATTLE ARENAS 2 Abstract Successful analysis of player skills in video games has important impacts on the process of enhancing player experience without undermining their continuous skill development. Moreover, player skill analysis becomes more intriguing in team-based video games because such form of study can help discover useful factors in effective team formation. In this paper, we consider the problem of skill decomposition in MOBA (MultiPlayer Online Battle Arena) games, with the goal to understand what player skill factors are essential for the outcome of a game match. To understand the construct of MOBA player skills, we utilize various skill-based predictive models to decompose player skills into interpretative parts, the impact of which are assessed in statistical terms. We apply this analysis approach on two widely known MOBAs, namely League of Legends (LoL) and Defense of the Ancients 2 (DOTA2). The finding is that base skills of in-game avatars, base skills of players, and players' champion-specific skills are three prominent skill components influencing LoL's match outcomes, while those of DOTA2 are mainly impacted by in-game avatars' base skills but not much by the other two. PLAYER SKILL DECOMPOSITION IN MULTIPLAYER ONLINE BATTLE ARENAS 3 Player Skill Decomposition in Multiplayer Online Battle Arenas Introduction Recently a unique type of sports, namely electronic sports (eSports), emerges as a popular genre of computer games, in which human players compete with one another in online, simulated environments governed by rules and regulations similar to those found in traditional forms of sports. A recent report released by SuperData (2016) showed that the worldwide market for eSports, by the end of 2015, has reached approximately 748 million dollars and is expected to grow to 1.9 billion dollars by 2019. Each team consisting of five players has a base to defend and the goal is to attack the opposite teams' champions and ultimately destroy the opponent's base.