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README.md
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type: PongNoFrameskip-v4
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metrics:
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- type: mean_reward
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value:
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name: mean_reward
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---
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@@ -129,7 +129,7 @@ from huggingface_ding import push_model_to_hub
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# Instantiate the agent
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agent = EfficientZeroAgent(env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-EfficientZero")
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# Train the agent
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return_ = agent.train(step=int(
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# Push model to huggingface hub
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push_model_to_hub(
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agent=agent.best,
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- **Demo:** [video](https://huggingface.co/OpenDILabCommunity/PongNoFrameskip-v4-EfficientZero/blob/main/replay.mp4)
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<!-- Provide the size information for the model. -->
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- **Parameters total size:** 33023.14 KB
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- **Last Update Date:** 2024-01-
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## Environments
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<!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. -->
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type: PongNoFrameskip-v4
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metrics:
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- type: mean_reward
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value: 20.4 +/- 0.66
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name: mean_reward
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---
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# Instantiate the agent
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agent = EfficientZeroAgent(env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-EfficientZero")
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# Train the agent
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return_ = agent.train(step=int(2000000))
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# Push model to huggingface hub
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push_model_to_hub(
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agent=agent.best,
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- **Demo:** [video](https://huggingface.co/OpenDILabCommunity/PongNoFrameskip-v4-EfficientZero/blob/main/replay.mp4)
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<!-- Provide the size information for the model. -->
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- **Parameters total size:** 33023.14 KB
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- **Last Update Date:** 2024-01-16
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## Environments
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<!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. -->
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