First ML-Agents project divide by zero error

I am following the codemonkey tutorial and my code is minimal at this point however I'm getting this error when I press play:
File "c:\users\USER\anaconda3\lib\site-packages\torch\nn\init.py", line 381, in kaiming_uniform_
std = gain / math.sqrt(fan)
ZeroDivisionError: float division by zero

The error message seems strange and I don't know if it's a problem with my installation of ml-agents or my project setup. Thanks for the help


Full trace:
[WARNING] Trainer has no policies, not saving anything.
Traceback (most recent call last):
File "c:\users\lmcck\anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\lmcck\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\lmcck\anaconda3\Scripts\mlagents-learn.exe\__main__.py", line 7, in <module>
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\learn.py", line 260, in main
run_cli(parse_command_line())
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\learn.py", line 256, in run_cli
run_training(run_seed, options, num_areas)
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\learn.py", line 132, in run_training
tc.start_learning(env_manager)
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped
return func(*args, **kwargs)
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\trainer_controller.py", line 173, in start_learning
self._reset_env(env_manager)
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped
return func(*args, **kwargs)
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\trainer_controller.py", line 107, in _reset_env
self._register_new_behaviors(env_manager, env_manager.first_step_infos)
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\trainer_controller.py", line 268, in _register_new_behaviors
self._create_trainers_and_managers(env_manager, new_behavior_ids)
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\trainer_controller.py", line 166, in _create_trainers_and_managers
self._create_trainer_and_manager(env_manager, behavior_id)
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\trainer_controller.py", line 137, in _create_trainer_and_manager
policy = trainer.create_policy(
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\trainer\rl_trainer.py", line 119, in create_policy
return self.create_torch_policy(parsed_behavior_id, behavior_spec)
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\ppo\trainer.py", line 227, in create_torch_policy
policy = TorchPolicy(
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\policy\torch_policy.py", line 57, in __init__
self.actor = SimpleActor(
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\torch\networks.py", line 606, in __init__
self.network_body = NetworkBody(observation_specs, network_settings)
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\torch\networks.py", line 212, in __init__
self._body_endoder = LinearEncoder(
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\torch\layers.py", line 148, in __init__
linear_layer(
File "c:\users\lmcck\anaconda3\lib\site-packages\mlagents\trainers\torch\layers.py", line 49, in linear_layer
layer = torch.nn.Linear(input_size, output_size)
File "c:\users\lmcck\anaconda3\lib\site-packages\torch\nn\modules\linear.py", line 83, in __init__
self.reset_parameters()
File "c:\users\lmcck\anaconda3\lib\site-packages\torch\nn\modules\linear.py", line 86, in reset_parameters
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
File "c:\users\lmcck\anaconda3\lib\site-packages\torch\nn\init.py", line 381, in kaiming_uniform_
std = gain / math.sqrt(fan)
ZeroDivisionError: float division by zero

 

Which version of ML agents are you using. And which version of com.unity.ml-agents are you using
For example I am using:
Version information:
ml-agents: 0.28.0,
ml-agents-envs: 0.28.0,
Communicator API: 1.5.0,
PyTorch: 1.7.1+cu110

and on the Unity Side:
com.unity.ml-agents@2.2.1-exp.1
com.unity.ml-agents.extensions@0.6.1-preview

If there is a mismatch in the paring of the Unity side version and the Python side version weird things can sometimes happen.

Note if you cloned followed the installation docs and cloned the github repository recently and got release 19. Then check that in Unity your not using v2.1.0-exp.1
instead of 2.2.1-exp.1

Otherwise check the sensors your using.

Just reading the stack trace the specific error is:
ML Agents is trying to initialize an NN Layer (linear layer) with most likely a Glorot variant maybe HE or Xavier etc... Anyway the initializer uses fan in and fan out parameters which are dependent on the shape of the layer tensor. In this case the fan parameter is zero, the square root of which is also zero. Since fan is derived from the tensor shape check your config yaml file. Also check that your sensors don't have an invalid configuration.

 

The super class of ZeroDivisionError is ArithmeticError. This exception raised when the second argument of a division or modulo operation is zero. In Mathematics, when a number is divided by a zero, the result is an infinite number. It is impossible to write an Infinite number physically. Python interpreter throws “ZeroDivisionError: division by zero” error if the result is infinite number. While implementing any program logic and there is division operation make sure always handle ArithmeticError or ZeroDivisionError so that program will not terminate.

try:

Code (CSharp):

1.     z = x / y
2. except ZeroDivisionError:
3.     z = 0

Or check before you do the division:

Code (CSharp):

1. if y == 0:
2.     z = 0
3. else:
4.     z = x / y

The latter can be reduced to:

Code (CSharp):

1. z = 0 if y == 0 else (x / y)

 

This was happening to me when I had my sensor component (Grid) and Agent Script component attached to two different game objects. When I put them on the same game object the problem was fixed.