agent_load.py

# Import required libraries
import gym_tanks
import gymnasium as gym
from stable_baselines3 import PPO
from multiprocessing import freeze_support

import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'

if __name__ == "__main__":
    # Avoid multiprocessing issues
    freeze_support()

    # Load and reset the environment
    env = gym.make('gym_tanks/tanks-v0')
    env.reset()

    TIMESTEPS = 10000

    # Set the models folder and path
    models_dir = "models/PPO/Test_Bot"
    model_path = f"{models_dir}/model_2064384_steps.zip" # Put here the weights file you want to read

    # Load the model
    model = PPO.load(model_path, env = env, n_steps = TIMESTEPS)

    # Vectorize the environment
    env = model.get_env()

    episodes = 10

    for ep in range(episodes):
        obs = env.reset()
        done = False
        while not done:
            # Render the environment
            env.render()
            # Predict the action
            action, _state = model.predict(obs, deterministic = False)
            # Step the environment
            obs, reward, done, info = env.step(action)
            print(reward)

    env.close()