how to build a good test bed?

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It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Skilgannon (talk)21:14, 27 September 2017

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Xor (talk)23:40, 27 September 2017

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Skilgannon (talk)06:25, 28 September 2017

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Xor (talk)07:57, 28 September 2017

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Skilgannon (talk)09:17, 28 September 2017
 

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Rsalesc (talk)21:30, 3 October 2017

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Skilgannon (talk)21:56, 3 October 2017

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Rsalesc (talk)22:07, 3 October 2017
 

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Rsalesc (talk)22:29, 3 October 2017

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Skilgannon (talk)10:10, 12 October 2017
 

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Xor (talk)02:33, 4 October 2017

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Rsalesc (talk)11:34, 4 October 2017
 

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Skilgannon (talk)10:09, 12 October 2017

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Xor (talk)15:05, 12 October 2017

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Skilgannon (talk)16:32, 12 October 2017

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Xor (talk)01:49, 13 October 2017
 
 
 
 
 
 

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Xor (talk)01:34, 31 May 2019

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Skilgannon (talk)11:09, 5 June 2019

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Xor (talk)06:16, 6 June 2019
 

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Xor (talk)09:59, 14 June 2019

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Skilgannon (talk)16:11, 16 June 2019
 
 
 
 
 

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Beaming (talk)00:33, 28 September 2017

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Xor (talk)01:18, 28 September 2017

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Beaming (talk)02:21, 28 September 2017

It depends what I am working in.

For movement, often a single bot is enough to prove a theory. Escape angle tuning is a rambot plus DevilFish, surfing mechanics is DoctorBob, anti-GF RaikoMicro, anti-fast-learning is Ascendant and for general unpredictability Shadow or Diamond.

Targeting I always find less interesting. Maybe because it is a more pure ML problem, with less ways to optimise that haven't already been studied in a related field. I decided to brute-force it by adding lots of features and then using a genetic optimization to tune the weights against recordings of the entire rumble population, about 5000 battles. The surfers I did separately, but with the same process.

Xor (talk)05:16, 28 September 2017
 
 
 
 
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