qboy21
commented
2 months ago Error:
AttributeError Traceback (most recent call last) Cell In[9], line 32 29 print(f'Deploy is finished, final epsiode return is: {returns}') 31 if name == "main": ---> 32 main(main_config=main_config, create_config=create_config, ckpt_path='/Users/qboy/Downloads/rl/final.pth.tar')
Cell In[9], line 14 12 main_config.exp_name = 'default' # Set the name of the experiment to be run in this deployment, which is the name of the project folder to be created 13 cfg = compile_config(main_config, create_cfg=create_config, auto=True) # Compile and generate all configurations ---> 14 env = DingEnvWrapper(gym.make(cfg.env.env_id), EasyDict(env_wrapper='default')) # Add the DI-engine environment decorator upon the gym's environment instance 15 #env.enable_save_replay(replay_path='./lunarlander_dqn_deploy/video') # Enable the video recording of the environment and set the video saving folder 16 model = DQN(**cfg.policy.model) # Import model configuration, instantiate DQN model
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/gym/envs/registration.py:581, in make(id, max_episode_steps, autoreset, apply_api_compatibility, disable_env_checker, **kwargs) 578 envcreator = spec.entry_point 579 else: 580 # Assume it's a string --> 581 envcreator = load(spec.entry_point) 583 mode = _kwargs.get("render_mode") 584 apply_human_rendering = False
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/gym/envs/registration.py:61, in load(name) 52 """Loads an environment with name and returns an environment creation function 53 54 Args: (...) 58 Calls the environment constructor 59 """ 60 mod_name, attr_name = name.split(":") ---> 61 mod = importlib.import_module(mod_name) 62 fn = getattr(mod, attr_name) 63 return fn
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/importlib/init.py:126, in import_module(name, package) 124 break 125 level += 1 --> 126 return _bootstrap._gcd_import(name[level:], package, level)
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AttributeError: module '_Box2D' has no attribute 'RAND_LIMIT_swigconstant'
Code (Exactly from tutorial example) import gym # Load the gym library, which is used to standardize the reinforcement learning environment import torch # Load the PyTorch library for loading the Tensor model and defining the computing network from easydict import EasyDict # Load EasyDict for instantiating configuration files from ding.config import compile_config # Load configuration related components in DI-engine config module from ding.envs import DingEnvWrapper # Load environment related components in DI-engine env module from ding.policy import DQNPolicy, single_env_forward_wrapper # Load policy-related components in DI-engine policy module from ding.model import DQN # Load model related components in DI-engine model module from dizoo.box2d.lunarlander.config.lunarlander_dqn_config import main_config, create_config # Load DI-zoo lunarlander environment and DQN algorithm related configurations
def main(main_config: EasyDict, create_config: EasyDict, ckpt_path: str): main_config.exp_name = 'default' # Set the name of the experiment to be run in this deployment, which is the name of the project folder to be created cfg = compile_config(main_config, create_cfg=create_config, auto=True) # Compile and generate all configurations env = DingEnvWrapper(gym.make(cfg.env.env_id), EasyDict(env_wrapper='default')) # Add the DI-engine environment decorator upon the gym's environment instance
env.enable_save_replay(replay_path='./lunarlander_dqn_deploy/video') # Enable the video recording of the environment and set the video saving folder
model = DQN(**cfg.policy.model) # Import model configuration, instantiate DQN model
state_dict = torch.load(ckpt_path, map_location='cpu') # Load model parameters from file
model.load_state_dict(state_dict['model']) # Load model parameters into the model
policy = DQNPolicy(cfg.policy, model=model).eval_mode # Import policy configuration, import model, instantiate DQN policy, and turn to evaluation mode
forward_fn = single_env_forward_wrapper(policy.forward) # Use the strategy decorator of the simple environment to decorate the decision method of the DQN strategy
obs = env.reset() # Reset the initialization environment to get the initial observations
returns = 1000. # Initialize total reward
while True: # Let the agent's strategy and environment interact cyclically until the end
action = forward_fn(obs) # According to the observed state, make a decision and generate action
obs, rew, done, info = env.step(action) # Execute actions, interact with the environment, get the next observation state, the reward of this interaction, the signal of whether to end, and other information
returns += rew # Cumulative reward return
if done:
break
print(f'Deploy is finished, final epsiode return is: {returns}')if name == "main": main(main_config=main_config, create_config=create_config, ckpt_path='/Users/qboy/Downloads/rl/final.pth.tar')
Attempted Fix Uninstalled Box2D and reinstalled with same issue
Overall, finding issues with most of the code examples. If you are not supporting the library anymore, no problem. Please state. Thank you.
PaParaZz1
Eric-Zhao1
docker运行的是ding:nightly版本，docker pull opendilab/ding:nightly。 运行使用 DQN 算法训练的智能体模型： final.pth.tar 报错如下：
有遇到过类似问题吗？