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---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 111.57 +/- 98.19
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **A2C** Agent playing **LunarLander-v2**
This is a trained model of a **A2C** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Training code (with Stable-baselines3)
```python
from stable_baselines3 import A2C
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 = 8
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
linear_schedule = lambda progress_remaining: progress_remaining * 0.00083
model = A2C(
"MlpPolicy",
env,
n_steps=5,
gamma=0.995,
learning_rate=linear_schedule,
ent_coef=0.00001,
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 = A2C.load("logs/best_model.zip")
```
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