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---
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: 264.63 +/- 19.16
name: mean_reward
verified: false
---
# **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)
TODO: Add your code
```python
import gymnasium as gym
# First, we create our environment called LunarLander-v2
env = gym.make("LunarLander-v2")
# Then we reset this environment
observation, info = env.reset()
for _ in range(20):
# Take a random action
action = env.action_space.sample()
print("Action taken:", action)
# Do this action in the environment and get
# next_state, reward, terminated, truncated and info
observation, reward, terminated, truncated, info = env.step(action)
# If the game is terminated (in our case we land, crashed) or truncated (timeout)
if terminated or truncated:
# Reset the environment
print("Environment is reset")
observation, info = env.reset()
env.close()
```
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