How to install Unity ML Agents Release 19 in 2022 and build your own Machine Learning project 

I have always had a question about mlagents: they randomly select actions at the beginning of training. Can we incorporate human intervention into the training process of mlagents to make them train faster? Is there a corresponding method in mlagents? Looking forward to your answer.

Thanks for the little introduction. It was very stupid to get all the right versions done but at the end it works and ill try out some stuff for my own projects :)

maybe you wont notice this comment,but i have to persist to say thank you bro,i successfully configured my mlagents env,but unfortunately due to new edition of unity or something else,i cant address all the errors mentioned in the console,but yeah,thank you again!

As of 2nd April, 2023, if anybody's getting PROTOBUF error or something like this while trying to train mlagents, run the command with environment activated "pip install protobuf==3.19.6"

I am using 2021.3.22f1 and I am getting The type or namespace name 'Newtonsoft' could not be found (are you missing a using directive or an assembly reference?)

richtig geil. Hatte alles wieder vergessen :D

just try Release 20 by follow your gide and it work! , Thanks

Thanks for this great tutorial. Minor detail, if it's the first time, you may have an error "Module onnx is not installed!". Just resolve it with : pip3 install onnx

Whenever I try to import Ml Agents folder in Project into the unity assets folder, it doesn't work it doesn't do anything absolutely anything

How would you install and train sac instead of ppo

Thank you for this aamazing video, really enjoy to follow. I already follow the instruction. However, I face a problem. When I start training the agent, the folder summaries cannot generated, so that I cannot move to visualize the result using tensorboard. Is there any steps I missing? Thank you.

Thank you very much. Great video. You helped me a lot. Only problem was with current numpy version, so I had to downgrade it to 1.23.3 but everything else is up to date.

Gracias bro... aunque antes había hecho varias preguntas, paso a paso las fui resolviendo, desde hace rato quería aprender a y utilizar MLAgent, pero siempre había un problema, pero este video me ayudo de muchas gracias, espero en un futuro puedas dar ejemplos haciendo proyectos desde 0. Un gran abrazo

Will you create more advanced videos on unity ml agents?

We need to load models that we download in runtime from a webserver, and we execute it in a build (not in Editor), is it possible? All the examples and code I find only work within Editor using the Resources Folder and the walk scene only works in the editor and we i try to build it, it reverts back to the default ONNX Model.

How did you stop the training at 9:15?

I keep getting the error: AttributeError: module 'numpy' has no attribute 'float' any idea what I can do to fix it?

when I add mlagent example asset to unity it said " The type or namespace name 'Newtonsoft' could not be found (are you missing a using directive or an assembly reference?)" how to fix it?

bro a query, how would I view the tensorflow statistics of the trained agents?

Be very observant on what version you have on everything you install. It does not always install the exact version you expect it to. My "file version" in the explorer(win) of python exe is 3.10.8150.1013 im not sure if this was the source of the problem but if the "file version" is an expression of the exact version of the program then 3.10.8150.1013 i just above the limit of an acceptable python versions for the mlagent package i think. I installed python 3.8 (.16) and it finally could build wheel with gym .toml files. the numPy version was 1.14.1 which just came automatically when i created a new conda environment with python 3.8.

Thank You!

release 20 is out and idk how to install it.

nice video, thank you

Hello I am getting an error while doing pip3 install -e ./ml-agents-envs note: This error originates from a subprocess, and is likely not a problem with pip. ERROR: Failed building wheel for numpy Failed to build numpy ERROR: Could not build wheels for numpy, which is required to install pyproject.toml-based projects

Do you need to download any python versions for this?

How do we stop the training properly at 9:12?

im tryna do that for more than 10 hours, 2 different types of tutorials and a LOT of github tips

It didn't work for me :(

I can’t import it for some reason 💀

If i wanna start it with "mlagents-learn config/ppo/PushBlock.yaml --run-id=push_block_test_01" this Message came ever "The "mlagents-learn" command is either misspelled or could not be found." I need help

@everyone !!very important!! date: 11 may 2023 you need to downgrade protobuf and numby. commands: pip install protobuf==3.19.6 pip install numby ==1.21.5 EDIT: I have one question: which onnx version do you use. when the training is finished It's a bit much and in german but further it said that i need a newer onnx version and then this happened [INFO] PushBlock. Step: 240000. Time Elapsed: 219.684 s. Mean Reward: 4.848. Std of Reward: 0.804. Training. [WARNING] Restarting worker[0] after 'Communicator has exited.' [INFO] Listening on port 5004. Start training by pressing the Play button in the Unity Editor. ============= Diagnostic Run torch.onnx.export version 2.0.1+cu118 ============= verbose: False, log level: Level.ERROR ======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ======================== [INFO] Exported results\push_block_test02\PushBlock\PushBlock-279713.onnx [INFO] Copied results\push_block_test02\PushBlock\PushBlock-279713.onnx to results\push_block_test02\PushBlock.onnx. Traceback (most recent call last): File "C:\Users\samue\anaconda3\envs\mlagents_R19\Scripts\mlagents-learn-script.py", line 33, in sys.exit(load_entry_point('mlagents', 'console_scripts', 'mlagents-learn')()) File "c:\users\samue\desktop\ml-agents-release_19\ml-agents\mlagents\trainers\learn.py", line 260, in main run_cli(parse_command_line()) File "c:\users\samue\desktop\ml-agents-release_19\ml-agents\mlagents\trainers\learn.py", line 256, in run_cli run_training(run_seed, options, num_areas) File "c:\users\samue\desktop\ml-agents-release_19\ml-agents\mlagents\trainers\learn.py", line 132, in run_training tc.start_learning(env_manager) File "c:\users\samue\desktop\ml-agents-release_19\ml-agents-envs\mlagents_envs\timers.py", line 305, in wrapped return func(*args, **kwargs) File "c:\users\samue\desktop\ml-agents-release_19\ml-agents\mlagents\trainers\trainer_controller.py", line 176, in start_learning n_steps = self.advance(env_manager) File "c:\users\samue\desktop\ml-agents-release_19\ml-agents-envs\mlagents_envs\timers.py", line 305, in wrapped return func(*args, **kwargs) File "c:\users\samue\desktop\ml-agents-release_19\ml-agents\mlagents\trainers\trainer_controller.py", line 234, in advance new_step_infos = env_manager.get_steps() File "c:\users\samue\desktop\ml-agents-release_19\ml-agents\mlagents\trainers\env_manager.py", line 124, in get_steps new_step_infos = self._step() File "c:\users\samue\desktop\ml-agents-release_19\ml-agents\mlagents\trainers\subprocess_env_manager.py", line 420, in _step self._restart_failed_workers(step) File "c:\users\samue\desktop\ml-agents-release_19\ml-agents\mlagents\trainers\subprocess_env_manager.py", line 328, in _restart_failed_workers self.reset(self.env_parameters) File "c:\users\samue\desktop\ml-agents-release_19\ml-agents\mlagents\trainers\env_manager.py", line 68, in reset self.first_step_infos = self._reset_env(config) File "c:\users\samue\desktop\ml-agents-release_19\ml-agents\mlagents\trainers\subprocess_env_manager.py", line 446, in _reset_env ew.previous_step = EnvironmentStep(ew.recv().payload, ew.worker_id, {}, {}) File "c:\users\samue\desktop\ml-agents-release_19\ml-agents\mlagents\trainers\subprocess_env_manager.py", line 101, in recv raise env_exception mlagents_envs.exception.UnityTimeOutException: The Unity environment took too long to respond. Make sure that : The environment does not need user interaction to launch The Agents' Behavior Parameters > Behavior Type is set to "Default" The environment and the Python interface have compatible versions. If you're running on a headless server without graphics support, turn off display by either passing --no-graphics option or build your Unity executable as server build.

*Hi, everything seems to be working as intended, thank you, I haven't started training yet, but after testing, right after it tells me results have been exported and copied I get the following traceback:* Traceback (most recent call last): File "C:\Users\Rabiie\.conda\envs\mlagents_r19_YT\Scripts\mlagents-learn-script.py", line 33, in sys.exit(load_entry_point('mlagents', 'console_scripts', 'mlagents-learn')()) File "c:\users\Riser\desktop\ml-agents-release_19\ml-agents-release_19\ml-agents\mlagents\trainers\learn.py", line 260, in main run_cli(parse_command_line()) File "c:\users\Riser\desktop\ml-agents-release_19\ml-agents-release_19\ml-agents\mlagents\trainers\learn.py", line 256, in run_cli run_training(run_seed, options, num_areas) File "c:\users\Riser\desktop\ml-agents-release_19\ml-agents-release_19\ml-agents\mlagents\trainers\learn.py", line 132, in run_training tc.start_learning(env_manager) File "c:\users\Riser\desktop\ml-agents-release_19\ml-agents-release_19\ml-agents-envs\mlagents_envs\timers.py", line 305, in wrapped return func(*args, **kwargs) File "c:\users\Riser\desktop\ml-agents-release_19\ml-agents-release_19\ml-agents\mlagents\trainers\trainer_controller.py", line 176, in start_learning n_steps = self.advance(env_manager) File "c:\users\Riser\desktop\ml-agents-release_19\ml-agents-release_19\ml-agents-envs\mlagents_envs\timers.py", line 305, in wrapped return func(*args, **kwargs) File "c:\users\Riser\desktop\ml-agents-release_19\ml-agents-release_19\ml-agents\mlagents\trainers\trainer_controller.py", line 234, in advance new_step_infos = env_manager.get_steps() File "c:\users\Riser\desktop\ml-agents-release_19\ml-agents-release_19\ml-agents\mlagents\trainers\env_manager.py", line 124, in get_steps new_step_infos = self._step() File "c:\users\Riser\desktop\ml-agents-release_19\ml-agents-release_19\ml-agents\mlagents\trainers\subprocess_env_manager.py", line 420, in _step self._restart_failed_workers(step) File "c:\users\Riser\desktop\ml-agents-release_19\ml-agents-release_19\ml-agents\mlagents\trainers\subprocess_env_manager.py", line 328, in _restart_failed_workers self.reset(self.env_parameters) File "c:\users\Riser\desktop\ml-agents-release_19\ml-agents-release_19\ml-agents\mlagents\trainers\env_manager.py", line 68, in reset self.first_step_infos = self._reset_env(config) File "c:\users\Riser\desktop\ml-agents-release_19\ml-agents-release_19\ml-agents\mlagents\trainers\subprocess_env_manager.py", line 446, in _reset_env ew.previous_step = EnvironmentStep(ew.recv().payload, ew.worker_id, {}, {}) File "c:\users\Riser\desktop\ml-agents-release_19\ml-agents-release_19\ml-agents\mlagents\trainers\subprocess_env_manager.py", line 101, in recv raise env_exception mlagents_envs.exception.UnityTimeOutException: The Unity environment took too long to respond. Make sure that : The environment does not need user interaction to launch The Agents' Behavior Parameters > Behavior Type is set to "Default" The environment and the Python interface have compatible versions. If you're running on a headless server without graphics support, turn off display by either passing --no-graphics option or build your Unity executable as server build. *This doesn't appear in your video, should I be worried?* *EDIT: I feel I should add I start learning by typing: **_"mlagents-learn --force"_** as I'm not using a Unity example in an empty project, but already have a project set up that I integrated MLAgents to.*