computer vision

ComputerVision/train_rl_model.py

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+import gymnasium as gym
+from stable_baselines3 import PPO
+from stable_baselines3.common.env_checker import check_env
+from robocup_ssl_env import RoboCupSSLEnv
+
+# Create environment
+env = RoboCupSSLEnv()
+
+# Check if the environment follows the Gym interface
+check_env(venv)
+
+# Instantiate the agent
+model = PPO('MlpPolicy', env, verbose=1)
+
+# Train the agent
+model.learn(total_timesteps=10000)
+
+# Save the model
+model.save("ppo_robocup_ssl")
+
+# To reload the trained model
+# model = PPO.load("ppo_robocup_ssl")
+
+# Evaluate the trained agent
+obs = env.reset()
+for _ in range(1000):
+    action, _states = model.predict(obs, deterministic=True)
+    obs, reward, done, info = env.step(action)
+    env.render()
+    if done:
+        obs = env.reset()
+
+env.close()