版本信息：
蟒蛇：3.10.4
ml-代理：0.30.0，
ml-agents-envs：0.30.0，
通讯器 API：1.5.0，
PyTorch：2.0.0+cu118
环境（Pip）包
absl-py==1.4.0
attrs==23.1.0
cachetools==5.3.0
cattrs==1.5.0
certifi==2022.12.7
charset-normalizer==3.1.0
cloudpickle==2.2.1
filelock==3.12.0
google-auth==2.17.3
google-auth-oauthlib==1.0.0
grpcio==1.54.0
gym==0.26.2
gym-notices==0.0.8
h5py==3.8.0
idna==3.4
Jinja2==3.1.2
Markdown==3.4.3
MarkupSafe==2.1.2
mlagents==0.30.0
mlagents-envs==0.30.0
mpmath==1.2.1
networkx==3.0
numpy==1.21.2
oauthlib==3.2.2
onnx==1.13.1
PettingZoo==1.15.0
Pillow==9.5.0
protobuf==3.20.3
pyasn1==0.5.0
pyasn1-modules==0.3.0
pypiwin32==223
pywin32==306
PyYAML==6.0
requests==2.29.0
requests-oauthlib==1.3.1
rsa==4.9
six==1.16.0
sympy==1.11.1
tensorboard==2.12.2
tensorboard-data-server==0.7.0
tensorboard-plugin-wit==1.8.1
torch==2.0.0+cu118
torchaudio==2.0.1+cu118
torchvision==0.15.1+cu118
typing_extensions==4.4.0
urllib3==1.26.15
Werkzeug==2.3.0
我一直在尝试训练 mlagents 示例时遇到问题，尤其是与 numpy 相关的示例。我在虚拟环境中运行 python。我正在运行 mlagents 示例中的 3DBall 示例，并且没有更改代码中的任何内容。我似乎无法让培训工作并继续以这个错误结束（控制台输出）：
[W ..\torch\csrc\utils\tensor_numpy.cpp:84] Warning: Failed to initialize NumPy: module compiled against API version 0x10 but this version of numpy is 0xe . Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem . (function operator ())
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Version information:
ml-agents: 0.30.0,
ml-agents-envs: 0.30.0,
Communicator API: 1.5.0,
PyTorch: 2.0.0+cu118
[W ..\torch\csrc\utils\tensor_numpy.cpp:84] Warning: Failed to initialize NumPy: module compiled against API version 0x10 but this version of numpy is 0xe . Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem . (function operator ())
[INFO] Listening on port 5004. Start training by pressing the Play button in the Unity Editor.
[INFO] Connected to Unity environment with package version 2.2.1-exp.1 and communication version 1.5.0
[INFO] Connected new brain: 3DBall?team=0
[WARNING] Deleting TensorBoard data events.out.tfevents.1682557176.DESKTOP-6DGKKSC.240.0 that was left over from a previous run.
[INFO] Hyperparameters for behavior name 3DBall:
trainer_type: ppo
hyperparameters:
batch_size: 64
buffer_size: 12000
learning_rate: 0.0003
beta: 0.001
epsilon: 0.2
lambd: 0.99
num_epoch: 3
shared_critic: False
learning_rate_schedule: linear
beta_schedule: linear
epsilon_schedule: linear
network_settings:
normalize: True
hidden_units: 128
num_layers: 2
vis_encode_type: simple
memory: None
goal_conditioning_type: hyper
deterministic: False
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
network_settings:
normalize: False
hidden_units: 128
num_layers: 2
vis_encode_type: simple
memory: None
goal_conditioning_type: hyper
deterministic: False
init_path: None
keep_checkpoints: 5
checkpoint_interval: 500000
max_steps: 500000
time_horizon: 1000
summary_freq: 12000
threaded: False
self_play: None
behavioral_cloning: None
============= Diagnostic Run torch.onnx.export version 2.0.0+cu118 =============
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================
[INFO] Exported results\yes\3DBall\3DBall-0.onnx
[INFO] Copied results\yes\3DBall\3DBall-0.onnx to results\yes\3DBall.onnx.
Traceback (most recent call last):
File "J:\Code\CS4100\finalproject\ml-agents\my_env\Scripts\mlagents-learn-script.py", line 33, in <module>
sys.exit(load_entry_point('mlagents==0.30.0', 'console_scripts', 'mlagents-learn')())
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents\trainers\learn.py", line 264, in main
run_cli(parse_command_line())
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents\trainers\learn.py", line 260, in run_cli
run_training(run_seed, options, num_areas)
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents\trainers\learn.py", line 136, in run_training
tc.start_learning(env_manager)
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped
return func(*args, **kwargs)
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents\trainers\trainer_controller.py", line 175, in start_learning
n_steps = self.advance(env_manager)
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped
return func(*args, **kwargs)
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents\trainers\trainer_controller.py", line 233, in advance
new_step_infos = env_manager.get_steps()
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents\trainers\env_manager.py", line 124, in get_steps
new_step_infos = self._step()
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents\trainers\subprocess_env_manager.py", line 408, in _step
self._queue_steps()
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents\trainers\subprocess_env_manager.py", line 302, in _queue_steps
env_action_info = self._take_step(env_worker.previous_step)
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped
return func(*args, **kwargs)
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents\trainers\subprocess_env_manager.py", line 543, in _take_step
all_action_info[brain_name] = self.policies[brain_name].get_action(
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents\trainers\policy\torch_policy.py", line 130, in get_action
run_out = self.evaluate(decision_requests, global_agent_ids)
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped
return func(*args, **kwargs)
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents\trainers\policy\torch_policy.py", line 94, in evaluate
tensor_obs = [torch.as_tensor(np_ob) for np_ob in obs]
File "J:\Code\CS4100\finalproject\ml-agents\my_env\lib\site-packages\mlagents\trainers\policy\torch_policy.py", line 94, in <listcomp>
tensor_obs = [torch.as_tensor(np_ob) for np_ob in obs]
RuntimeError: Could not infer dtype of numpy.float32