Big neural network (units: [2048, 1024, 1024, 512])
10000 steps
Running on top of Oige env simulation (constant for each run)
Skrl uses single forward pass implementation
Library
Mixed-Precision
Time (s)
slowing factor Base: rlgames, mixed pr. = True
RLGames
No
448
1.322x
RLGames
Yes
339
1 (base)
SKRL
No
475
1.401x
SKRL
Yes
373
1.1x
SKRL
Yes *
358
1.056x
* in this run mixed precision was used also for inference during data collection phase
Quality eval:
We trained a policy for our task with each of the configurations multiple times. We didn’t observe any statistically significant difference in quality of the final results.
Mixed precision
Motivation:
Inspired by RLGames, we implemented automatic mixed double precision to boost performance of PPO.
Sources:
https://pytorch.org/docs/stable/amp.html
https://pytorch.org/docs/stable/notes/amp_examples.html
Speed eval:
Big neural network (units: [2048, 1024, 1024, 512])
10000 steps
Running on top of Oige env simulation (constant for each run)
Skrl uses single forward pass implementation
* in this run mixed precision was used also for inference during data collection phase
Quality eval: