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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: 282.88 +/- 14.89
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)
```
from typing import Callable
def linear_schedule(initial_value: float) -> Callable[[float], float]:
def func(progress_remaining: float) -> float:
return progress_remaining * initial_value
return func
model = PPO(policy="MlpPolicy", env=env, verbose=1, n_epochs=10, learning_rate=linear_schedule(0.005), n_steps=1500)
``` |