Update README.md

README.md

@@ -3,7 +3,6 @@ license: apache-2.0
 ---
 # Dataset Card for Pong-v4-expert-MCTS
 ## Table of Contents
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 - [Supported Tasks and Baseline](#support-tasks-and-baseline)
 
 - [Data Usage](#data-usage)
@@ -24,10 +23,8 @@ license: apache-2.0
   - [Contributions](##Contributions)
 
 ## Supported Tasks and Baseline
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 - This dataset supports the training for [Procedure Cloning (PC )](https://arxiv.org/abs/2205.10816) algorithm.
 - Baselines when sequence length for decision is 0:
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 | Train loss                                         | Test Acc | Reward |
 | -------------------------------------------------- | -------- | ------ |
 | ![feature](./sup_loss.png) | 0.90     | 20     |
@@ -36,13 +33,9 @@ license: apache-2.0
 | ----------------------------------------------------- | ------------------------------------------------- | --------------------------------------------------- | ------ |
 | ![feature](./action_loss.png) | ![feature](./hs_loss.png) | ![feature](./train_acc.png) | -21    |
 ## 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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 - `obs`: An Array3D containing observations from 8 trajectories of an evaluated agent. The data type is uint8 and each value is in 0 to 255. The shape of this tensor is [96, 96, 3], that is, the channel dimension in the last dimension. 
 - `actions`: An integer containing actions from 8 trajectories of an evaluated agent. This value is from 0 to 5. Details about this environment can be viewed at [Pong - Gym Documentation](https://www.gymlibrary.dev/environments/atari/pong/).
 - `hidden_state`: An Array3D containing corresponding hidden states generated by EfficientZero, from 8 trajectories of an evaluated agent. The data type is float32.
@@ -70,7 +63,6 @@ def generate_examples(self, filepath):
 There is only a training set for this dataset, as evaluation is undertaken by interacting with a simulator.
 
 ### Initial Data Collection and Normalization
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 - This dataset is collected by EfficientZero policy.
 - The standard for expert data is that each return of 8 episodes is over 20.
 - No normalization is previously applied ( i.e. each value of observation is a uint8 scalar in [0, 255] )