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README.md
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@@ -21,7 +21,7 @@ model-index:
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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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@@ -149,7 +149,7 @@ pip3 install LightZero
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repo_id="OpenDILabCommunity/PongNoFrameskip-v4-EfficientZero",
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platform_info="[LightZero](https://github.com/opendilab/LightZero) and [DI-engine](https://github.com/opendilab/di-engine)",
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model_description="**LightZero** is an efficient, easy-to-understand open-source toolkit that merges Monte Carlo Tree Search (MCTS) with Deep Reinforcement Learning (RL), simplifying their integration for developers and researchers. More details are in paper [LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios](https://huggingface.co/papers/2310.08348).",
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create_repo=
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)
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```
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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: 5.8 +/- 4.92
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name: mean_reward
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---
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repo_id="OpenDILabCommunity/PongNoFrameskip-v4-EfficientZero",
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platform_info="[LightZero](https://github.com/opendilab/LightZero) and [DI-engine](https://github.com/opendilab/di-engine)",
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model_description="**LightZero** is an efficient, easy-to-understand open-source toolkit that merges Monte Carlo Tree Search (MCTS) with Deep Reinforcement Learning (RL), simplifying their integration for developers and researchers. More details are in paper [LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios](https://huggingface.co/papers/2310.08348).",
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create_repo=False
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)
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```
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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:** 2024-01-08
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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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