tinkoff-ai / lb-sac Goto Github PK
View Code? Open in Web Editor NEWOfficial implementation for "Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size", NeurIPS 2022, Offline RL Workshop
License: Apache License 2.0
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wuti0525lb-sac's Issues
Some questions about the results of the LB-SAC paper report:
- When I conducted walker2d-full-replay-v2 experiment, final normalized score was around 106.5, while 109.1 was reported in the paper. Although there is not a big difference, due to my improvement of this algorithm, The result is around 108. I want to know whether this result really improves the algorithm performance, or whether I should write the reproduced results together in the paper, similar to the halfcheetah-medium-expert-v2 results, although the reproduced results are slightly worse than those in the paper. However, my improved results are still slightly worse than the results reported in the paper, but higher than the original algorithm results reproduced by myself. Since this algorithm is still the most advanced of the model-free algorithms, the room for performance improvement is very limited, so small improvements to it are also very important to me.
- About the calculation method of normalized score for your two reports, final normalized score refers to the final policy score, which I understand the average result of the algorithm in the last step on the 4 random seeds. For Normalized maximum scores, I understand it in two ways：
1. Record the best score on each seed for average.
2. Maximum average result obtained in the same steps.
Do you average first and then look for the best score or do you look for the best score first and then average?
Looking forward to your early reply!
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