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a2c_cartpole_v1.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2dee6997223d845912eed3add6a2874bb689705c87fe9819b0954bcb4791a405
+size 93582

a2c_sb3_cartpole.py

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+import gym
+import stable_baselines3
+from stable_baselines3 import A2C
+import matplotlib.pyplot as plt
+
+# Initialize the environment
+env = gym.make("CartPole-v1")
+
+# Define the A2C model and learn
+model = A2C("MlpPolicy", env, verbose=1)
+model.learn(total_timesteps=10000)
+
+# Save the model
+model.save("a2c_cartpole_v1")
+
+rewards_by_episodes = []
+cum_reward = 0
+
+# Test the trained model and output the reward
+obs = env.reset()
+for i in range(2000):
+    action, _states = model.predict(obs)
+    obs, rewards, dones, info = env.step(action)
+    env.render()
+    if rewards == 1.0:
+        cum_reward += 1
+    if dones:
+        rewards_by_episodes.append(cum_reward)
+        env.reset()
+        cum_reward = 0
+
+env.close()
+
+# define x axis for plot :
+x = list(range(len(rewards_by_episodes)))
+
+# Plot rewards by episodes
+plt.figure("Figure 1")
+plt.xlabel("Episodes")
+plt.ylabel("Reward")
+plt.plot(x, rewards_by_episodes)
+plt.savefig("a2c_cartpole_v1.png")
+plt.title("Rewards by episodes for SB3-A2C algorithm")
+plt.show()