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Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning

Vlad Firoiu, William F. Whitney, Joshua B. Tenenbaum

2017 · arXiv

Machine learningPsychologyAlgorithms
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From the abstract

There has been a recent explosion in the capabilities of game-playing artificial intelligence. Many classes of RL tasks, from Atari games to motor control to board games, are now solvable by fairly generic algorithms, based on deep learning, that learn to play from experience with minimal knowledge of the specific domain of interest. In this work, we will investigate the performance of these methods on Super Smash Bros. Melee (SSBM), a popular console fighting game. The SSBM environment has complex dynamics and partial observability, making it challenging for human and machine alike. The multi-player aspect poses an additional challenge, as the vast majority of recent advances in RL have focused on single-agent environments. Nonetheless, we will show that it is possible to train agents that are competitive against and even surpass human professionals, a new result for the multi-player video game setting.

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Original HN comments, with labels from simple text rules. A label describes the whole comment and may miss context or irony. Read the discussion to make up your own mind. About the labels

3 selected excerpts · 3 total mentions

minimaxir
Neutral mention · rule
> We’ve increased the reaction time of OpenAI Five from 80ms to 200ms. This reaction time is much closer to human level, though we haven’t seen evidence of changes in gameplay as Openai Five’s strength comes more from teamwork and coordination than reflexes. This new constraint is interesting. The Super Smash Bros. Melee AI paper noted that they had to keep the reaction times to superhuman levels in order for the model to converge (albeit DOTA is a bit different from Melee): https://arxiv.org/abs/1702.06230
Original comment OpenAI Five Benchmark
chis
Neutral mention · rule
I was pretty impressed by the Falcon AI earlier this year. He actually used modern reinforcement learning to train from 0 instead of the crappy built in AI. Also, his AI was limited to near-human reaction time (10 frames) which led to a more human-looking agent. At the end of the day these papers are from master's students. I think they're both impressive in that context. https://arxiv.org/pdf/1702.06230.pdf
Original comment Super Smash Bros. Melee: An “Untouchable” Agent
ilitirit
Neutral mention · rule
> I wonder if knowing the different strategies would make pro-humans better at playing the game. Kinda. Some of the AI strategies are based on the behaviour of human players. For example, in the later versions when Ryu dizzies the opponent, he will whiff several jabs. This is widely regarded as a tribute to a combovideo maker known as TZW[1] who often did this in his videos. With regards to actual Fighting Game strategy, the AI in Super Street Fighter II absolutely uses tactics employed by real players. These are a few off the top of my head: - Tick throws - Whiff aerial attacks into command grab - Footsie spacing into whiff punish - Fake footsie spacing into grab -…
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Low/Overhead attack mixup - Left/Right attack mixup Using Lua scripting, a compatible emulator and a rudimentary neural network it's completely possible to build a "better" AI. I believe a more advanced one was recently created for Super Smash Bros [2]. 1. https://www.youtube.com/watch?v=M0CidJaVS0Q#t=4m48s 2. https://arxiv.org/abs/1702.06230
Original comment Street Fighter II's AI Engine

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