The Obstacle Tower Challenge is the task to master a procedurally generated chain of levels that subsequently get harder to complete. Whereas the top 6 performing entries of last year's competition all used human demonstrations to learn how to cope with the challenge, we present an approach that performed competitively (placed 7th) but starts completely from scratch by means of Deep Reinforcement Learning with a relatively simple feed-forward deep network structure. We especially look at the generalization performance of the taken approach concerning different seeds and various visual themes that have become available after the competition, and investigate where the agent fails and why. Note that our approach does not possess a short-term memory like employing recurrent hidden states. With this work, we hope to contribute to a better understanding of what is possible with a relatively simple, flexible solution that can be applied to learning in environments featuring complex 3D visual input where the abstract task structure itself is still fairly simple.

After six months of competition (and a few last-minute submissions), we are happy to announce the conclusion and winners of the Obstacle Tower Challenge. We want to thank all of the participants for both rounds and congratulate Alex Nichol, the Compscience.org We are also excited to share that we have open-sourced Obstacle Tower for the research community to extend for their own needs. We started this challenge in February as a way to help foster research in the AI community, by providing a challenging new benchmark of agent performance built in Unity, which we called Obstacle Tower. The Obstacle Tower was developed to be difficult for current machine learning algorithms to solve, and push the boundaries of what was possible in the field by focusing on procedural generation. Key to that was only allowing participants access to one hundred instances of the Obstacle Tower, and evaluating their trained agents on a set of unique procedurally generated towers they had never seen before.

After six months of competition, Unity Technologies has named the top winners for the Obstacle Tower Challenge, a contest dedicated to making artificial intelligence for games. The winners are Alex Nichols, the Compscience.org And Unity, the maker of the Unity3D game engine, has also announced it has open-sourced Obstacle Tower for the research community to extend for their own needs. The challenge started in February as a way to help foster research in the AI community by providing a challenging new benchmark built in Unity. The benchmark, called Obstacle Tower, was developed to be difficult for current machine learning algorithms to solve.

Video game developers have longed used artificial intelligence to help create believable worlds. So it's not too surprising that researchers can now use some of those same game-making tools to train AI. During a talk at VentureBeat's Transform 2019 conference last week, Unity Technologies VP of AI and machine learning Danny Lange argued that game engines are perfect for creating what he called "real" computer intelligence -- self-learning systems capable of producing complex behaviors after a short amount of time. With game engines (like the company's own Unity engine), you can simulate the rules of the real world and test intelligent agents against it. "If you think about [it], the game engine has three dimensions, time, physics … it has everything you need to play around with the core elements that led to [human] intelligence," said Lange.

The rapid pace of research in Deep Reinforcement Learning has been driven by the presence of fast and challenging simulation environments. These environments often take the form of games; with tasks ranging from simple board games, to classic home console games, to modern strategy games. We propose a new benchmark called Obstacle Tower: a high visual fidelity, 3D, 3rd person, procedurally generated game environment. An agent in the Obstacle Tower must learn to solve both low-level control and high-level planning problems in tandem while learning from pixels and a sparse reward signal. Unlike other similar benchmarks such as the ALE, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment. In this paper we outline the environment and provide a set of initial baseline results produced by current state-of-the-art Deep RL methods as well as human players. In all cases these algorithms fail to produce agents capable of performing anywhere near human level on a set of evaluations designed to test both memorization and generalization ability. As such, we believe that the Obstacle Tower has the potential to serve as a helpful Deep RL benchmark now and into the future.

Unity, a leading maker of game development tools, announced today that it's created a new, unprecedented type of video game: one designed not to be played by humans, but by artificial intelligence. The game is called Obstacle Tower, and it's a piece of software created to judge the level of sophistication of an AI agent by measuring how efficiently it can maneuver up to 100 levels that change and scale in difficulty in unpredictable ways. Each level is procedurally generated, so it changes every time the AI attempts it. With Obstacle Tower, and a $100,000 pool of prizes set aside for participants to claim as part of a contest, Unity hopes it can provide AI researchers with a new benchmarking tool to evaluate self-learning software. "We wanted to give the researchers something to really work with that would to an extreme degree challenge the abilities of the AI systems that are currently in research and development around the world," Danny Lange, Unity's vice president of AI and machine learning, told The Verge.