Upload a2c_sb3_cartpole.py

a2c_sb3_cartpole.py

@@ -0,0 +1,30 @@
+import gym
+
+from stable_baselines3 import A2C
+
+# Set up the CartPole environement
+# Create the environment
+env = gym.make("CartPole-v1")
+
+# Reset the environment and get the initial observation
+observation = env.reset()
+
+print('observation space:', env.observation_space)
+print('action space:', env.action_space)
+print('threshold: ', env.spec.reward_threshold)
+
+
+model = A2C("MlpPolicy", env, verbose=1)
+print(model)
+model.learn(total_timesteps=25000)
+
+
+for _ in range(500):
+    observation = env.reset()
+    done = False
+
+    while not done:
+        action, _observations = model.predict(observation)
+        observation, reward, done, info = env.step(action)
+
+    env.render()