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# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env seals/HalfCheetah-v0 -orga HumanCompatibleAI -f logs/
python enjoy.py --algo ppo --env seals/HalfCheetah-v0 -f logs/pip install rl_zoo3), from anywhere you can do:python -m rl_zoo3.load_from_hub --algo ppo --env seals/HalfCheetah-v0 -orga HumanCompatibleAI -f logs/
rl_zoo3 enjoy --algo ppo --env seals/HalfCheetah-v0 -f logs/python train.py --algo ppo --env seals/HalfCheetah-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env seals/HalfCheetah-v0 -f logs/ -orga HumanCompatibleAI1OrderedDict([('batch_size', 64),
2 ('clip_range', 0.1),
3 ('ent_coef', 3.794797423594763e-06),
4 ('gae_lambda', 0.95),
5 ('gamma', 0.95),
6 ('learning_rate', 0.0003286871805949382),
7 ('max_grad_norm', 0.8),
8 ('n_envs', 1),
9 ('n_epochs', 5),
10 ('n_steps', 512),
11 ('n_timesteps', 1000000.0),
12 ('normalize',
13 {'gamma': 0.95, 'norm_obs': False, 'norm_reward': True}),
14 ('policy', 'MlpPolicy'),
15 ('policy_kwargs',
16 {'activation_fn': <class 'torch.nn.modules.activation.Tanh'>,
17 'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>,
18 'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}),
19 ('vf_coef', 0.11483689492120866),
20 ('normalize_kwargs',
21 {'norm_obs': {'gamma': 0.95,
22 'norm_obs': False,
23 'norm_reward': True},
24 'norm_reward': False})])