Datasets incorrect? Expert data rewards seem worse than medium data about d4rl HOT 3 CLOSED

Amortizing over a trajectory length of 1000 (standard for gym), it seems like the medium dataset is collected with a policy getting a score of approx 3470 which is very close to optimal. On the other hand, the expert dataset is collected with a policy of score 1500, which seems closer to a medium sub-optimal policy.

I'm guessing the datasets have been named incorrectly when uploading?

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Thanks - I swapped the two datasets. Clean the downloaded datasets (`rm ~/.d4rl/datasets/hopper*v1.hdf5') and you can download them again.

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I have a similar question about hopper_medium_replay:

env = gym.make('hopper-random-v1')
dataset = env.get_dataset()
print(np.mean(dataset['rewards']))  # 0.8286486

env = gym.make('hopper-medium-v1')
dataset = env.get_dataset()
print(np.mean(dataset['rewards']))  # 1.5018191

env = gym.make('hopper-expert-v1')
dataset = env.get_dataset()
print(np.mean(dataset['rewards']))  # 3.466414

env = gym.make('hopper-medium-replay-v1')
dataset = env.get_dataset()
print(np.mean(dataset['rewards']))  # 3.0534504202260146

I don't think it makes sense for medium replay to have a higher per-step reward than medium.

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