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--- |
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library_name: stable-baselines3 |
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tags: |
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- AntBulletEnv-v0 |
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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: AntBulletEnv-v0 |
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type: AntBulletEnv-v0 |
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metrics: |
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- type: mean_reward |
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value: 1834.41 +/- 107.15 |
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name: mean_reward |
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verified: false |
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--- |
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# **A2C** Agent playing **AntBulletEnv-v0** |
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This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** |
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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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TODO: Add your code |
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```python |
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import pybullet_envs |
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import panda_gym |
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import gym |
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import os |
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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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#Environment 1: AntBulletEnv-v0 |
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env_id = "AntBulletEnv-v0" |
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# Create the env |
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env = gym.make(env_id) |
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env = make_vec_env(env_id, n_envs=4) |
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# Adding this wrapper to normalize the observation and the reward |
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env = VecNormalize(env, norm_obs=True, norm_reward=True, clip_obs=10) |
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#create A2C model |
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model = A2C(policy = "MlpPolicy", |
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env = env, |
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gae_lambda = 0.9, |
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gamma = 0.99, |
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learning_rate = 0.00096, |
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max_grad_norm = 0.5, |
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n_steps = 8, |
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vf_coef = 0.4, |
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ent_coef = 0.0, |
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seed=11, |
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policy_kwargs=dict( |
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log_std_init=-2, ortho_init=False), |
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normalize_advantage=False, |
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use_rms_prop= True, |
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use_sde= True, |
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verbose=1) |
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#train agent |
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model.learn(1_500_000) |
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# Save the model and VecNormalize statistics when saving the agent |
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model.save("a2c-AntBulletEnv-v0") |
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env.save("vec_normalize.pkl") |
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``` |
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