Progress for week 12 (2020)
From Robin
(Difference between revisions)
(Ny side: ==Nikolai== * Laget en online questionnaire ** https://docs.google.com/forms/d/e/1FAIpQLSed_zAZwY_p64z7WXnulcUzJp9MPJ5hriegSJ1FpZy6-ogDcA/viewform?usp=sf_link * Google form ikke særlig bra…) |
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** https://docs.google.com/forms/d/e/1FAIpQLSed_zAZwY_p64z7WXnulcUzJp9MPJ5hriegSJ1FpZy6-ogDcA/viewform?usp=sf_link | ** https://docs.google.com/forms/d/e/1FAIpQLSed_zAZwY_p64z7WXnulcUzJp9MPJ5hriegSJ1FpZy6-ogDcA/viewform?usp=sf_link | ||
* Google form ikke særlig bra på video, må kanskje bruke noe annet | * Google form ikke særlig bra på video, må kanskje bruke noe annet | ||
+ | |||
+ | == Wonho == | ||
+ | === Accounting === | ||
+ | Ran most of the experiments BUT | ||
+ | * while comparing fitness only vs ns only vs moea - fitness only actually had highest fitness/distance traveled despite statistically not significant | ||
+ | ** Turned out that last position bonus to fitness at the end of the run is enough to push the search in right direction | ||
+ | ** running experiments without it shows clear performance change for fitness only but similiar for ns only | ||
+ | |||
+ | [[fil:Comparison_of_Mean-Best_Distance_Traveled.png]] | ||
+ | |||
+ | ** This suggests that MBF measurement is somewhat flawed, for beh.descriptor experiment - it has other consequence | ||
+ | [[fil:Mean-Best_Fitness_Comparison_between_Behavior_Descriptors.png]] | ||
+ | [[fil:Comparison_of_Mean-Best_Distance_Traveled_for_Behavior_Descriptors.png]] | ||
+ | |||
+ | * Static controller evolved with MOEA performing too well. | ||
+ | ** Considering only best genomes, it can handle unseen leg configurations and also walks as far as flexible ones(statistically insignificant). | ||
+ | ** although fitness only run does perform better - in terms of walking gait & number of genomes managing all unseen leg configs - hard to directly compare as they are evolved in different EA scheme | ||
+ | |||
+ | * While writing implementation, found out some flaws in controller implementation. | ||
+ | ** CTRNN output is limited to [0-1] due to sigmoid activation at output layer | ||
+ | ** At some point in the past it was scaled to [-pi/2, pi/2], which is input range DyRET env can take | ||
+ | ** this scaling has been gone missing whilst changing packages/merging/branching/testing | ||
+ | ** result is that leg movement is even more constrained than I have intended to. Possibly reason why static controller is also performing well on unseen leg configurations. | ||
+ | |||
+ | === Budget === | ||
+ | * Continue writing |
Current revision as of 09:42, 19 March 2020
Contents |
Nikolai
- Laget en online questionnaire
- Google form ikke særlig bra på video, må kanskje bruke noe annet
Wonho
Accounting
Ran most of the experiments BUT
- while comparing fitness only vs ns only vs moea - fitness only actually had highest fitness/distance traveled despite statistically not significant
- Turned out that last position bonus to fitness at the end of the run is enough to push the search in right direction
- running experiments without it shows clear performance change for fitness only but similiar for ns only
fil:Comparison_of_Mean-Best_Distance_Traveled.png
- This suggests that MBF measurement is somewhat flawed, for beh.descriptor experiment - it has other consequence
fil:Mean-Best_Fitness_Comparison_between_Behavior_Descriptors.png fil:Comparison_of_Mean-Best_Distance_Traveled_for_Behavior_Descriptors.png
- Static controller evolved with MOEA performing too well.
- Considering only best genomes, it can handle unseen leg configurations and also walks as far as flexible ones(statistically insignificant).
- although fitness only run does perform better - in terms of walking gait & number of genomes managing all unseen leg configs - hard to directly compare as they are evolved in different EA scheme
- While writing implementation, found out some flaws in controller implementation.
- CTRNN output is limited to [0-1] due to sigmoid activation at output layer
- At some point in the past it was scaled to [-pi/2, pi/2], which is input range DyRET env can take
- this scaling has been gone missing whilst changing packages/merging/branching/testing
- result is that leg movement is even more constrained than I have intended to. Possibly reason why static controller is also performing well on unseen leg configurations.
Budget
- Continue writing