eval.py

import gym
import numpy as np

import jsbsim
import gym_jsbsim

from stable_baselines.ddpg.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines.ddpg.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise, AdaptiveParamNoiseSpec
from stable_baselines import DDPG

env = gym.make('JSBSim-TurnHeadingControlTask-Cessna172P-Shaping.STANDARD-FG-v0')
env = DummyVecEnv([lambda: env])

# the noise objects for DDPG
n_actions = env.action_space.shape[-1]
param_noise = None
action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions))

model = DDPG.load("model/ddpg_fg_turnheading", env=env, tensorboard_log="model/tensorboard/")

obs = env.reset()
while True:
    action, _states = model.predict(obs)
    obs, rewards, dones, info = env.step(action)
    env.render(mode='flightgear')