
# **PPO** Agent playing **AntBulletEnv-v0**
This is a trained model of a **PPO** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).

## Usage (with Stable-baselines3)

```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub

...
```
MODEL
model = PPO(policy = "MlpPolicy",
            env = env,
            batch_size = 256,
            clip_range = 0.4,
            ent_coef = 0.0,
            gae_lambda = 0.92,
            gamma = 0.99,
            learning_rate = 3.0e-05,
            max_grad_norm = 0.5,
            n_epochs = 30,
            n_steps = 512,
            policy_kwargs = dict(log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, net_arch=[dict(pi=[256,
      256], vf=[256, 256])] ),
            use_sde = True,
            sde_sample_freq = 4,
            vf_coef = 0.5,
            tensorboard_log = "./tensorboard",
            verbose=1)
            
model.learn(1_000_000)
{"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntBulletEnv-v0"}, "metrics": [{"type": "mean_reward", "value": "2447.40 +/- 23.14", "name": "mean_reward"}]}]}]}
stable-baselines3 AntBulletEnv-v0 deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 


# PPO Agent playing AntBulletEnv-v0
This is a trained model of a PPO agent playing AntBulletEnv-v0
using the stable-baselines3 library.

## Usage (with Stable-baselines3)


MODEL
model = PPO(policy = "MlpPolicy",
            env = env,
            batch_size = 256,
            clip_range = 0.4,
            ent_coef = 0.0,
            gae_lambda = 0.92,
            gamma = 0.99,
            learning_rate = 3.0e-05,
            max_grad_norm = 0.5,
            n_epochs = 30,
            n_steps = 512,
            policy_kwargs = dict(log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, net_arch=[dict(pi=[256,
      256], vf=[256, 256])] ),
            use_sde = True,
            sde_sample_freq = 4,
            vf_coef = 0.5,
            tensorboard_log = "./tensorboard",
            verbose=1)
            
URL(1_000_000)
# PPO Agent playing AntBulletEnv-v0
This is a trained model of a PPO agent playing AntBulletEnv-v0
using the stable-baselines3 library. ## Usage (with Stable-baselines3)


MODEL
model = PPO(policy = "MlpPolicy",
            env = env,
            batch_size = 256,
            clip_range = 0.4,
            ent_coef = 0.0,
            gae_lambda = 0.92,
            gamma = 0.99,
            learning_rate = 3.0e-05,
            max_grad_norm = 0.5,
            n_epochs = 30,
            n_steps = 512,
            policy_kwargs = dict(log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, net_arch=[dict(pi=[256,
      256], vf=[256, 256])] ),
            use_sde = True,
            sde_sample_freq = 4,
            vf_coef = 0.5,
            tensorboard_log = "./tensorboard",
            verbose=1)
            
URL(1_000_000)
TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # PPO Agent playing AntBulletEnv-v0
This is a trained model of a PPO agent playing AntBulletEnv-v0
using the stable-baselines3 library. ## Usage (with Stable-baselines3)


MODEL
model = PPO(policy = "MlpPolicy",
            env = env,
            batch_size = 256,
            clip_range = 0.4,
            ent_coef = 0.0,
            gae_lambda = 0.92,
            gamma = 0.99,
            learning_rate = 3.0e-05,
            max_grad_norm = 0.5,
            n_epochs = 30,
            n_steps = 512,
            policy_kwargs = dict(log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, net_arch=[dict(pi=[256,
      256], vf=[256, 256])] ),
            use_sde = True,
            sde_sample_freq = 4,
            vf_coef = 0.5,
            tensorboard_log = "./tensorboard",
            verbose=1)
            
URL(1_000_000)
passage: TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# PPO Agent playing AntBulletEnv-v0
This is a trained model of a PPO agent playing AntBulletEnv-v0
using the stable-baselines3 library.## Usage (with Stable-baselines3)


MODEL
model = PPO(policy = "MlpPolicy",
            env = env,
            batch_size = 256,
            clip_range = 0.4,
            ent_coef = 0.0,
            gae_lambda = 0.92,
            gamma = 0.99,
            learning_rate = 3.0e-05,
            max_grad_norm = 0.5,
            n_epochs = 30,
            n_steps = 512,
            policy_kwargs = dict(log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, net_arch=[dict(pi=[256,
      256], vf=[256, 256])] ),
            use_sde = True,
            sde_sample_freq = 4,
            vf_coef = 0.5,
            tensorboard_log = "./tensorboard",
            verbose=1)
            
URL(1_000_000)