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Update hands-on.mdx

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Abhishek Rana
2024-05-01 16:59:33 +05:30
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@@ -478,7 +478,7 @@ mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, d
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
```
- In my case, I got a mean reward is `200.20 +/- 20.80` after training for 1 million steps, which means that our lunar lander agent is ready to land on the moon 🌛🥳.
- In my case, I got a mean reward of `200.20 +/- 20.80` after training for 1 million steps, which means that our lunar lander agent is ready to land on the moon 🌛🥳.
## Publish our trained model on the Hub 🔥
Now that we saw we got good results after the training, we can publish our trained model on the hub 🤗 with one line of code.