I taught the model via

if __name__ == "__main__":
    env = CustomEnv(possible_action_clicks)
    check_env(env)

    policy_kwargs = dict(
    net_arch=dict(pi=[2048, 1024, 512], vf=[2048, 1024, 512]),
    activation_fn=torch.nn.GELU,
    optimizer_class=optim.RAdam,
)

    model = PPO('MlpPolicy', env, verbose=0, tensorboard_log=logdir, device='cuda', batch_size=2000,
                policy_kwargs=policy_kwargs, ent_coef=0.1, learning_rate=0.00001, gamma=0.9, 
                use_sde=True)

    TIMESTEPS = 100000
    vec_env = model.get_env()
    observation = vec_env.reset()
    done = False

    while not done:
        model.learn(total_timesteps=TIMESTEPS, reset_num_timesteps=False, tb_log_name=f"PPO")
        model.save(f"{models_dir}/model_{int(time.time())}")

        action, _ = model.predict(observation)
        
        observation, reward, terminated, truncated, info = env.step(action)

        if terminated or truncated:
            vec_env = model.get_env()
            observation = vec_env.reset()

but when I try to load, it always retruns 1 in every prediction

model_path = "D:/modelsPPO/1722422450/model_1722458138.zip"
model = PPO('MlpPolicy', env, verbose=0, tensorboard_log=logdir, device='cuda', batch_size=2000,
            policy_kwargs=policy_kwargs, ent_coef=0.1, learning_rate=0.00001, gamma=0.9, 
            use_sde=True)

# Load the model with the environment
env = CustomEnv(possible_action_clicks)
model = model.load(model_path, env=env, verbose=1) c

Could you help me with that?

I have tried different load methods, but all failed