
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).

## Usage (with Stable-baselines3)

# Create the environment
env = make_vec_env("LunarLander-v2", n_envs=16)

# Defining the model, we use MultiLayerPerceptron (MLPPolicy) because the input is a vector,
# if we had frames as input we would use CnnPolicy
model = PPO(
    policy="MlpPolicy",
    env=env,
    n_steps=1024,
    batch_size=64,
    n_epochs=4,
    gamma=0.999,
    gae_lambda=0.98,
    ent_coef=0.01,
    verbose=1,
)

# Training the model for 3,000,000 timesteps
model.learn(total_timesteps=3000000)
# Save the model
model_name = "ppo-LunarLander-v2"
model.save(model_name)
```

{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "291.20 +/- 18.45", "name": "mean_reward"}]}]}]}
stable-baselines3 LunarLander-v2 deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 


# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.

## Usage (with Stable-baselines3)

# Create the environment
env = make_vec_env("LunarLander-v2", n_envs=16)

# Defining the model, we use MultiLayerPerceptron (MLPPolicy) because the input is a vector,
# if we had frames as input we would use CnnPolicy
model = PPO(
    policy="MlpPolicy",
    env=env,
    n_steps=1024,
    batch_size=64,
    n_epochs=4,
    gamma=0.999,
    gae_lambda=0.98,
    ent_coef=0.01,
    verbose=1,
)

# Training the model for 3,000,000 timesteps
URL(total_timesteps=3000000)
# Save the model
model_name = "ppo-LunarLander-v2"
URL(model_name)
'''

# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library. ## Usage (with Stable-baselines3) # Create the environment
env = make_vec_env("LunarLander-v2", n_envs=16) # Defining the model, we use MultiLayerPerceptron (MLPPolicy) because the input is a vector, # if we had frames as input we would use CnnPolicy
model = PPO(
    policy="MlpPolicy",
    env=env,
    n_steps=1024,
    batch_size=64,
    n_epochs=4,
    gamma=0.999,
    gae_lambda=0.98,
    ent_coef=0.01,
    verbose=1,
) # Training the model for 3,000,000 timesteps
URL(total_timesteps=3000000) # Save the model
model_name = "ppo-LunarLander-v2"
URL(model_name)
'''
TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library. ## Usage (with Stable-baselines3) # Create the environment
env = make_vec_env("LunarLander-v2", n_envs=16) # Defining the model, we use MultiLayerPerceptron (MLPPolicy) because the input is a vector, # if we had frames as input we would use CnnPolicy
model = PPO(
    policy="MlpPolicy",
    env=env,
    n_steps=1024,
    batch_size=64,
    n_epochs=4,
    gamma=0.999,
    gae_lambda=0.98,
    ent_coef=0.01,
    verbose=1,
) # Training the model for 3,000,000 timesteps
URL(total_timesteps=3000000) # Save the model
model_name = "ppo-LunarLander-v2"
URL(model_name)
'''
TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.## Usage (with Stable-baselines3)# Create the environment
env = make_vec_env("LunarLander-v2", n_envs=16)# Defining the model, we use MultiLayerPerceptron (MLPPolicy) because the input is a vector,# if we had frames as input we would use CnnPolicy
model = PPO(
    policy="MlpPolicy",
    env=env,
    n_steps=1024,
    batch_size=64,
    n_epochs=4,
    gamma=0.999,
    gae_lambda=0.98,
    ent_coef=0.01,
    verbose=1,
)# Training the model for 3,000,000 timesteps
URL(total_timesteps=3000000)# Save the model
model_name = "ppo-LunarLander-v2"
URL(model_name)
'''