add sb3 example

examples/carl_with_sb3.py

@@ -0,0 +1,38 @@
+import carl
+import gymnasium as gym
+from gymnasium.wrappers import FlattenObservation
+from stable_baselines3 import DQN
+from stable_baselines3.common.evaluation import evaluate_policy
+
+from carl.envs import CARLLunarLander
+from carl.context.context_space import NormalFloatContextFeature
+from carl.context.sampler import ContextSampler
+
+# Create environment
+context_distributions = [NormalFloatContextFeature("GRAVITY_X", mu=9.8, sigma=1)]
+context_sampler = ContextSampler(
+        context_distributions=context_distributions,
+        context_space=CARLLunarLander.get_context_space(),
+        seed=42,
+    )
+contexts = context_sampler.sample_contexts(n_contexts=5)
+
+print("Training contexts are:")
+print(contexts)
+
+env = gym.make("carl/CARLLunarLander-v0", render_mode="rgb_array", contexts=contexts)
+env = FlattenObservation(env)
+
+# Instantiate the agent
+model = DQN("MlpPolicy", env, verbose=1)
+# Train the agent and display a progress bar
+model.learn(total_timesteps=int(2e4), progress_bar=True)
+mean_reward, std_reward = evaluate_policy(model, model.get_env(), n_eval_episodes=10)
+
+# Enjoy trained agent
+vec_env = model.get_env()
+obs = vec_env.reset()
+for i in range(1000):
+    action, _states = model.predict(obs, deterministic=True)
+    obs, rewards, dones, info = vec_env.step(action)
+    vec_env.render("human")

setup.py

@@ -56,6 +56,9 @@ def read_file(filepath: str) -> str:
         "sphinx-autoapi>=1.8.4",
         "automl-sphinx-theme>=0.1.9",
     ],
+    "examples": [
+        "stable-baselines3",
+    ]
 }
 
 setuptools.setup(