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# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env HopperBulletEnv-v0 -orga sb3 -f logs/
python enjoy.py --algo ppo --env HopperBulletEnv-v0 -f logs/python train.py --algo ppo --env HopperBulletEnv-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env HopperBulletEnv-v0 -f logs/ -orga sb31OrderedDict([('batch_size', 128),
2 ('clip_range', 'lin_0.4'),
3 ('ent_coef', 0.0),
4 ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
5 ('gae_lambda', 0.92),
6 ('gamma', 0.99),
7 ('learning_rate', 3e-05),
8 ('max_grad_norm', 0.5),
9 ('n_envs', 16),
10 ('n_epochs', 20),
11 ('n_steps', 512),
12 ('n_timesteps', 2000000.0),
13 ('normalize', True),
14 ('policy', 'MlpPolicy'),
15 ('policy_kwargs',
16 'dict(log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, '
17 'net_arch=[dict(pi=[256, 256], vf=[256, 256])] )'),
18 ('sde_sample_freq', 4),
19 ('use_sde', True),
20 ('vf_coef', 0.5),
21 ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])