|
--- |
|
library_name: stable-baselines3 |
|
tags: |
|
- LunarLander-v2 |
|
- deep-reinforcement-learning |
|
- reinforcement-learning |
|
- stable-baselines3 |
|
model-index: |
|
- name: PPO |
|
results: |
|
- metrics: |
|
- type: mean_reward |
|
value: 291.20 +/- 18.45 |
|
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) |
|
|
|
# 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) |
|
``` |
|
|
