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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: 282.71 +/- 20.92
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)
## Parameters
model = PPO( <br>
policy = "MlpPolicy", <br>
env = env, <br>
learning_rate = 0.0001, <br>
n_steps = 1024, <br>
batch_size = 32, <br>
n_epochs = 16, <br>
gamma = 0.999, <br>
ent_coef = 0.01, <br>
verbose = 1 <br>
) <br>
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