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---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 280.22 +/- 13.03
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
```python
from stable_baselines3 import DQN
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.callbacks import EvalCallback
# Create the environment
env_id = "LunarLander-v2"
n_envs = 1
env = make_vec_env(env_id, n_envs=n_envs)
# Create the evaluation envs
eval_envs = make_vec_env(env_id, n_envs=5)
# Adjust evaluation interval depending on the number of envs
eval_freq = int(1e5)
eval_freq = max(eval_freq // n_envs, 1)
# Create evaluation callback to save best model
# and monitor agent performance
eval_callback = EvalCallback(
eval_envs,
best_model_save_path="./logs/",
eval_freq=eval_freq,
n_eval_episodes=10,
)
# Instantiate the agent
# Hyperparameters from https://github.com/DLR-RM/rl-baselines3-zoo
model = DQN(
"MlpPolicy",
env,
learning_starts=0,
batch_size=128,
buffer_size=50000,
learning_rate=1e-3,
target_update_interval=250,
train_freq=4,
gradient_steps=-1,
# Explore for 20_000 timesteps
exploration_fraction=0.04,
exploration_final_eps=0.1,
policy_kwargs=dict(net_arch=[256, 256]),
verbose=1,
)
# Train the agent (you can kill it before using ctrl+c)
try:
model.learn(total_timesteps=int(5e5), callback=eval_callback)
except KeyboardInterrupt:
pass
# Load best model
model = DQN.load("logs/best_model.zip")
```
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