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README.md

@@ -21,7 +21,7 @@ model-index:
       type: CartPole-v0
     metrics:
     - type: mean_reward
-      value: 200.0 +/- 0.0
+      value: 199.4 +/- 1.8
       name: mean_reward
 ---
 
@@ -29,7 +29,7 @@ model-index:
 
 ## Model Description
 <!-- Provide a longer summary of what this model is. -->
-This is a simple **MuZero** implementation to OpenAI/Gym/Box2d **CartPole-v0** using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo).
+This is a simple **MuZero** implementation to OpenAI/Gym/Box2d **CartPole-v0** by using pytorch.
 
 **DI-engine** is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported  by reusing different abstraction levels of DI-engine reinforcement learning framework.
 
@@ -284,7 +284,7 @@ exp_config = {
 - **Demo:** [video](https://huggingface.co/OpenDILabCommunity/CartPole-v0-MuZero/blob/main/replay.mp4)
 <!-- Provide the size information for the model. -->
 - **Parameters total size:** 13548.13 KB
-- **Last Update Date:** 2023-11-30
+- **Last Update Date:** 2023-12-05
 
 ## Environments
 <!-- 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. -->