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---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 280.00 +/- 24.62
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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)
```
model = PPO(
policy = 'MlpPolicy',
env = env,
n_steps = 2048,
batch_size = 512,
n_epochs = 4,
gamma = 0.099,
gae_lambda = 0.98,
ent_coef = 0.01,
learning_rate=0.00001,
verbose=1,
tensorboard_log="./ppo_tensorboard/")
model.learn(total_timesteps=int(10e6))
```
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