Comments (2)
Hi, I think you're right. I trained the decision transformer in maze2d-medium-dense-v1 environment and calculated the normalized score with this command: env.get_normalized_score(average return of 100 episodes)
. However, I obtained a score of 56, which does not align with the reported maximum score of 35 in the paper " QDT: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL".
I wanted to know if you have calculated the expert score for maze2d-medium-dense-v1?
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Hi, I think you're right. I trained the decision transformer in maze2d-medium-dense-v1 environment and calculated the normalized score with this command:
env.get_normalized_score(average return of 100 episodes)
. However, I obtained a score of 56, which does not align with the reported maximum score of 35 in the paper " QDT: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL". I wanted to know if you have calculated the expert score for maze2d-medium-dense-v1?
Hi, I'm also attempting to calculate normalized score with command: env.get_normalized_score(average return of 100 episodes)
in antmaze task , but can't get correct score repported in the paper. Have you found a solution to this issue?
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