所以基本上我尝试从https://github.com/Gor-Ren/gym-jsbsim转换这个自定义健身房环境。使用farama基金会的gymnasium api。这是我正在处理的存储库:https://github.com/sryu1/jsbgym 当我尝试使用gym-jsbsim 训练环境时，它可以工作，但是对于gymnasium 环境，我收到标题中的错误...我觉得tasks.py 中的第234 行到242 行是导致问题的地方。所有算法都会出现相同的错误...如果有人能告诉我我做错了什么，我将非常感激! 我尝试使用稳定的基线 3 所有支持 Box 的算法来训练自定义环境，但它们都遇到了相同的错误。
这是我的原始格式的 ipynb，如果你只是用记事本打开一个新的 ipynb 文件，然后将其粘贴进去，然后再次打开，它应该可以工作。
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "7e5a3876",
"metadata": {},
"outputs": [],
"source": [
"import jsbsim\n",
"import gymnasium as gym\n",
"import jsbgym\n",
"import os\n",
"from stable_baselines3 import DDPG\n",
"from stable_baselines3.common.callbacks import BaseCallback\n",
"from stable_baselines3.common.monitor import Monitor\n",
"from stable_baselines3.common.vec_env import DummyVecEnv, VecFrameStack"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c78d0a36",
"metadata": {},
"outputs": [],
"source": [
"env = gym.make('JSBSim-TurnHeadingControlTask-Cessna172P-Shaping.STANDARD-NoFG-v0')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "17ea6f3e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 5.00000000e+03, -1.52383093e-16, 1.16583160e-16, 2.02536000e+02,\n",
" -4.26325641e-14, -7.10542736e-15, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, -3.72529030e-09, -9.68166768e-15, -1.60633375e+02,\n",
" 2.99000000e+02]),\n",
" {})"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"env.reset()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "60a7ecab",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total Reward for episode 1 is 24.43895374007404\n",
"Total Reward for episode 2 is 17.88229242588352\n",
"Total Reward for episode 3 is 20.844080298653026\n",
"Total Reward for episode 4 is 23.09412403738447\n",
"Total Reward for episode 5 is 22.540357474496297\n"
]
}
],
"source": [
"for episode in range(1, 6):\n",
" obs = env.reset()\n",
" done = False\n",
" total_reward = 0\n",
" while not done:\n",
" obs, reward, done, _, info = env.step(env.action_space.sample())\n",
" total_reward += reward\n",
" print(\"Total Reward for episode {} is {}\".format(episode, total_reward))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d819a741",
"metadata": {},
"outputs": [],
"source": [
"class TrainAndLoggingCallback(BaseCallback):\n",
" def __init__(self, check_freq, save_path, verbose=1):\n",
" super(TrainAndLoggingCallback, self).__init__(verbose)\n",
" self.check_freq = check_freq\n",
" self.save_path = save_path\n",
"\n",
" def _init_callback(self):\n",
" if self.save_path is not None:\n",
" os.makedirs(self.save_path, exist_ok=True)\n",
"\n",
" def _on_step(self):\n",
" if self.n_calls % self.check_freq == 0:\n",
" model_path = os.path.join(\n",
" self.save_path, \"best_model_{}\".format(self.n_calls)\n",
" )\n",
" self.model.save(model_path)\n",
"\n",
" return True"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a0f962d6",
"metadata": {},
"outputs": [],
"source": [
"CHECKPOINT_DIR = \"./train/\"\n",
"LOG_DIR = \"./logs/\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d2ab0033",
"metadata": {},
"outputs": [],
"source": [
"callback = TrainAndLoggingCallback(check_freq=1000000, save_path=CHECKPOINT_DIR)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "ea1fd388",
"metadata": {},
"outputs": [
{
"ename": "AssertionError",
"evalue": "The algorithm only supports <class 'gym.spaces.box.Box'> as action spaces but Box(-1.0, 1.0, (3,), float64) was provided",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_17260\\3532872291.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDDPG\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"MlpPolicy\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtensorboard_log\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mLOG_DIR\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\Users\\Noah Ryu\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\stable_baselines3\\ddpg\\ddpg.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, policy, env, learning_rate, buffer_size, learning_starts, batch_size, tau, gamma, train_freq, gradient_steps, action_noise, replay_buffer_class, replay_buffer_kwargs, optimize_memory_usage, tensorboard_log, policy_kwargs, verbose, seed, device, _init_setup_model)\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[0mtarget_noise_clip\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 104\u001b[0m \u001b[0mtarget_policy_noise\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 105\u001b[1;33m \u001b[0m_init_setup_model\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 106\u001b[0m )\n\u001b[0;32m 107\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Users\\Noah Ryu\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\stable_baselines3\\td3\\td3.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, policy, env, learning_rate, buffer_size, learning_starts, batch_size, tau, gamma, train_freq, gradient_steps, action_noise, replay_buffer_class, replay_buffer_kwargs, optimize_memory_usage, policy_delay, target_policy_noise, target_noise_clip, tensorboard_log, policy_kwargs, verbose, seed, device, _init_setup_model)\u001b[0m\n\u001b[0;32m 118\u001b[0m \u001b[0moptimize_memory_usage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moptimize_memory_usage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 119\u001b[0m \u001b[0msupported_action_spaces\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mspaces\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBox\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 120\u001b[1;33m \u001b[0msupport_multi_env\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 121\u001b[0m )\n\u001b[0;32m 122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Users\\Noah Ryu\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\stable_baselines3\\common\\off_policy_algorithm.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, policy, env, learning_rate, buffer_size, learning_starts, batch_size, tau, gamma, train_freq, gradient_steps, action_noise, replay_buffer_class, replay_buffer_kwargs, optimize_memory_usage, policy_kwargs, tensorboard_log, verbose, device, support_multi_env, monitor_wrapper, seed, use_sde, sde_sample_freq, use_sde_at_warmup, sde_support, supported_action_spaces)\u001b[0m\n\u001b[0;32m 117\u001b[0m \u001b[0muse_sde\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_sde\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 118\u001b[0m \u001b[0msde_sample_freq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msde_sample_freq\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 119\u001b[1;33m \u001b[0msupported_action_spaces\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msupported_action_spaces\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 120\u001b[0m )\n\u001b[0;32m 121\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbuffer_size\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Users\\Noah Ryu\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\stable_baselines3\\common\\base_class.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, policy, env, learning_rate, policy_kwargs, tensorboard_log, verbose, device, support_multi_env, monitor_wrapper, seed, use_sde, sde_sample_freq, supported_action_spaces)\u001b[0m\n\u001b[0;32m 171\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0msupported_action_spaces\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 172\u001b[0m assert isinstance(self.action_space, supported_action_spaces), (\n\u001b[1;32m--> 173\u001b[1;33m \u001b[1;34mf\"The algorithm only supports {supported_action_spaces} as action spaces \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 174\u001b[0m \u001b[1;34mf\"but {self.action_space} was provided\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 175\u001b[0m )\n",
"\u001b[1;31mAssertionError\u001b[0m: The algorithm only supports <class 'gym.spaces.box.Box'> as action spaces but Box(-1.0, 1.0, (3,), float64) was provided"
]
}
],
"source": [
"model = DDPG(\"MlpPolicy\", env, tensorboard_log=LOG_DIR)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "b80a3ed4",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_17260\\1190813584.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlearn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10000000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"JSBSim_10000000_steps\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'model' is not defined"
]
}
],
"source": [
"model.learn(total_timesteps=10000000, callback=callback)\n",
"model.save(\"JSBSim_10000000_steps\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "72842db2",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_17260\\4222837208.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mtotal_reward\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[0mobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mtotal_reward\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'model' is not defined"
]
}
],
"source": [
"for episode in range(5):\n",
" obs = env.reset()\n",
" done = False\n",
" total_reward = 0\n",
" while not done:\n",
" action, _ = model.predict(obs)\n",
" obs, reward, done, info = env.step(int(action))\n",
" total_reward += reward\n",
" print(\"Total Reward for episode {} is {}\".format(episode, total_reward))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f0daa1ab",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
},
"vscode": {
"interpreter": {
"hash": "fc676d0716d313b34d9b58671be5ff89ed5ca710c84a0894db60f3144580aba8"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
最佳答案
Stable Baselines 3 ，至少在 1.7.0 之前，取决于 gym，而不是其较新的 gymnasium 等效项。
尽管 importgymnasium asgy 应该可以在您自己的代码中实现此目的，但某些 Stable Baselines3 代码仍然执行导入，例如(例如，请参阅 td3.py):
from gym import spaces
并使用gym空间来验证您的gymnasium环境的操作空间。
您可以通过运行 type(env.action_space) 来检查自己，您会看到它返回 gymnasium.spaces.box.Box 而不是 gym .spaces.box.Box.
您可以阅读 this PR 的评论了解有关 future 潜在解决方案的更多详细信息。
关于python - 提供了断言错误 : The algorithm only supports <class 'gym.spaces.box.Box' > as action spaces but Box(-1. 0, 1.0, (3,), float32)，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/75108957/