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Recipe: AILearnsToPlayFoldIt4.lua
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Name: AILearnsToPlayFoldIt4.lua
ID: 103361
Created on: Sat, 04/04/2020 - 02:39
Updated on: Sat, 04/04/2020 - 10:15
Description:

Show terminal output and comment your evolved algorithm hashes of action number strands (1 through 9, A, Base 11 digits). Now tests your own algorithm first and doesn't randomize it initially. mutateSidechains takes a long time (2 to 4 minutes) but on non-designer puzzles (prediction puzzles) the AI agents figure out mutateSidechains is not necessary. Also note that while the agents are mutating the sidechains, they are also mutating themselves. That is one way of Machine Learning. It is a script that use genetic algorithm to find actions in game that gain score and adjust them over time. Source code can be found here: https://github.com/Grommii/Foldit/blob/master/AILearnsToPlayFoldIt.lua

Adjustable parameters: **Population Size** - Number of species in population. In other words - number of top species that survive to the next generation; **Mutation Size** - Number of mutated algorithms that will be added and checked each generation; **Aliens Size** - Number of random algorithms that will be added and checked each generation; **Cross Size** - Number of crossed algorithms that will be added and checked each generation; **Number Of Algorithm Steps** - Number of actions (genes) that can perform each algorithm; **Iteration Score Threshold** - Score gain threshold for algorithm to be executed again during evaluation; **Reset World Generation Each** - Number of generations that will be tested based on the same starting position before new top position will be saved as start point; **Mutate Rate** - Probability of new gene during mutation;

Also you can check/uncheck pre-defined algorithms to start with. By default two common algorithms included.



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Joined: 10/10/2015
Groups: None
汉字 test

汉字 test

Joined: 10/10/2015
Groups: None
note: for now, A in the

note: for now, A in the hashes needs to be capitalized

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Developed by: UW Center for Game Science, UW Institute for Protein Design, Northeastern University, Vanderbilt University Meiler Lab, UC Davis
Supported by: DARPA, NSF, NIH, HHMI, Amazon, Microsoft, Adobe, RosettaCommons