Team Aborg is a solo effort from Alexandre Borghi. In addition to these modifications, the team also tried a number of things that didn’t pan out: DeepMimic, object detection through YOLO, and some Sonic-specific ideas. First, it uses RGB images rather than grayscale second, it uses a slightly augmented action space, with more common button combinations third, it uses an augmented reward function, which rewards the agent for visiting new states (as judged by a perceptual hash of the screen). In recent years, they have studied how to apply reinforcement learning to real world problems, especially in an e-commerce setting, together with Yang Yu, who is an Associate Professor of the Department of Computer Science at Nanjing University, Nanjing, China.ĭharmaraja’s solution is a variant of joint PPO (described in our tech report) with a few improvements. Qing Da and Anxiang Zeng are from the AI team within the search department of Alibaba in Hangzhou, China. mistake's learning curves end early because they timed out at 12 hours.ĭharmaraja is a six-member team including Qing Da, Jing-Cheng Shi, Anxiang Zeng, Guangda Huzhang, Run-Ze Li, and Yang Yu. As we will describe in more detail below, these two teams fine-tuned (using PPO) from a pre-trained network, whereas mistake trained from scratch (using Rainbow DQN). Note that Dharmaraja and aborg start at similar scores, whereas mistake starts much lower. Learning curves of the top three teams for all 11 levels are as follows (showing the standard error computed from three runs).Īveraging over all levels, we can see the following learning curves. The top three teams will receive trophies. (From top to bottom, ordered by score on the level: Dharmaraja, aborg and mistake)ĭharmaraja topped the scoreboard during the contest, and the lead remained on the final evaluation mistake narrowly won out over aborg for second place. Red dots indicate earlier episodes whereas blue dots indicate later episodes. Three different AIs learning on the same level.

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