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Pong-v4-expert-MCTS

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kxzxvbk commited on Apr 13, 2023

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@@ -30,19 +30,19 @@ license: apache-2.0
 | Train loss                                         | Test Acc | Reward |
 | -------------------------------------------------- | -------- | ------ |
-| <img src="https://huggingface.co/datasets/OpenDILabCommunity/Pong-v4-expert-MCTS/blob/main/img/sup_loss.png" style="zoom:50%;" /> | 0.90     | 20     |
 - Baselines when sequence length for decision is 4:
 | Train action loss                                     | Train hidden state loss                           | Train acc (auto-regressive mode)                    | Reward |
 | ----------------------------------------------------- | ------------------------------------------------- | --------------------------------------------------- | ------ |
-| <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    |
 ## Data Usage
 ### Data description
-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.
 ### Data Fields

 | Train loss                                         | Test Acc | Reward |
 | -------------------------------------------------- | -------- | ------ |
+| <img src="./img/sup_loss.png" style="zoom:50%;" /> | 0.90     | 20     |
 - Baselines when sequence length for decision is 4:
 | Train action loss                                     | Train hidden state loss                           | Train acc (auto-regressive mode)                    | Reward |
 | ----------------------------------------------------- | ------------------------------------------------- | --------------------------------------------------- | ------ |
+| <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    |
 ## Data Usage
 ### Data description
+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.
 ### Data Fields