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@@ -30,19 +30,19 @@ license: apache-2.0
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  | Train loss | Test Acc | Reward |
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  | -------------------------------------------------- | -------- | ------ |
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- | <img src="https://huggingface.co/datasets/OpenDILabCommunity/Pong-v4-expert-MCTS/blob/main/img/sup_loss.png" style="zoom:50%;" /> | 0.90 | 20 |
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  - Baselines when sequence length for decision is 4:
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  | Train action loss | Train hidden state loss | Train acc (auto-regressive mode) | Reward |
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  | ----------------------------------------------------- | ------------------------------------------------- | --------------------------------------------------- | ------ |
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- | <img src="https://huggingface.co/datasets/OpenDILabCommunity/Pong-v4-expert-MCTS/blob/main/img/action_loss.png" style="zoom:50%;" /> | <img src="https://huggingface.co/datasets/OpenDILabCommunity/Pong-v4-expert-MCTS/blob/main/img/hs_loss.png" style="zoom:50%;" /> | <img src="https://huggingface.co/datasets/OpenDILabCommunity/Pong-v4-expert-MCTS/blob/main/img/train_acc.png" style="zoom:50%;" /> | -21 |
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  ## Data Usage
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  ### Data description
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- This dataset includes 8 episodes of pong-v4 environment. The expert policy is [EfficientZero]([[2111.00210\] Mastering Atari Games with Limited Data (arxiv.org)](https://arxiv.org/abs/2111.00210)), which is able to generate MCTS hidden states. Because of the contained hidden states for each observation, this dataset is suitable for Imitation Learning methods that learn from a sequence like PC.
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  ### Data Fields
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  | Train loss | Test Acc | Reward |
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  | -------------------------------------------------- | -------- | ------ |
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+ | <img src="./img/sup_loss.png" style="zoom:50%;" /> | 0.90 | 20 |
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  - Baselines when sequence length for decision is 4:
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  | Train action loss | Train hidden state loss | Train acc (auto-regressive mode) | Reward |
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  | ----------------------------------------------------- | ------------------------------------------------- | --------------------------------------------------- | ------ |
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+ | <img src="./img/action_loss.png" style="zoom:50%;" /> | <img src="./img/hs_loss.png" style="zoom:50%;" /> | <img src="./img/train_acc.png" style="zoom:50%;" /> | -21 |
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  ## Data Usage
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  ### Data description
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+ This dataset includes 8 episodes of pong-v4 environment. The expert policy is [EfficientZero](https://arxiv.org/abs/2111.00210), which is able to generate MCTS hidden states. Because of the contained hidden states for each observation, this dataset is suitable for Imitation Learning methods that learn from a sequence like PC.
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  ### Data Fields
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