a2c_sb3_cartpole.py

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()