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--- |
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library_name: stable-baselines3 |
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tags: |
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- PandaReachDense-v3 |
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- deep-reinforcement-learning |
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- reinforcement-learning |
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- stable-baselines3 |
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model-index: |
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- name: A2C |
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results: |
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- task: |
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type: reinforcement-learning |
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name: reinforcement-learning |
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dataset: |
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name: PandaReachDense-v3 |
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type: PandaReachDense-v3 |
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metrics: |
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- type: mean_reward |
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value: -0.14 +/- 0.09 |
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name: mean_reward |
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verified: false |
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--- |
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# **A2C** Agent playing **PandaReachDense-v3** |
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This is a trained model of a **A2C** agent playing **PandaReachDense-v3** |
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). |
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## Usage (with Stable-baselines3) |
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```python |
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import os |
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import gymnasium as gym |
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import panda_gym |
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from huggingface_sb3 import load_from_hub, package_to_hub |
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from stable_baselines3 import A2C |
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from stable_baselines3.common.evaluation import evaluate_policy |
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from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize |
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from stable_baselines3.common.env_util import make_vec_env |
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from huggingface_hub import notebook_login |
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``` |
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**Environment** |
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```python |
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env_id = "PandaReachDense-v3" |
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# Create the env |
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env = gym.make(env_id) |
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``` |
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**Model** |
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```python |
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model = A2C(policy = "MultiInputPolicy", |
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env = env, |
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learning_rate = 0.0001, |
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n_steps = 10, |
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verbose=1) |
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``` |