Question Agent is not learning due to PyTorch error?

This is my first time working with MLAgents and machine learning in general, and I thought I did everything right but the agent has been going for a while now and I'm not seeing any improvements, I'm also getting a lot warnings that I'm not sure what to make of.

Spoiler: LOG

(venv) D:\Projects\Games\Warsh>mlagents-learn --force

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╓╖╬│││││┘ ╬│││││╬╖
╖╬│││││╬╜ ╙╬│││││╖╖ ╗╗╗
╬╬╬╬╖││╦╖ ╖╬││╗╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╜╜╜ ╟╣╣
╬╬╬╬╬╬╬╬╖│╬╖╖╓╬╪│╓╣╣╣╣╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╒╣╣╖╗╣╣╣╗ ╣╣╣ ╣╣╣╣╣╣ ╟╣╣╖ ╣╣╣
╬╬╬╬┐ ╙╬╬╬╬│╓╣╣╣╝╜ ╫╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╟╣╣╣╙ ╙╣╣╣ ╣╣╣ ╙╟╣╣╜╙ ╫╣╣ ╟╣╣
╬╬╬╬┐ ╙╬╬╣╣ ╫╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╟╣╣╬ ╣╣╣ ╣╣╣ ╟╣╣ ╣╣╣┌╣╣╜
╬╬╬╜ ╬╬╣╣ ╙╝╣╣╬ ╙╣╣╣╗╖╓╗╣╣╣╜ ╟╣╣╬ ╣╣╣ ╣╣╣ ╟╣╣╦╓ ╣╣╣╣╣
╙ ╓╦╖ ╬╬╣╣ ╓╗╗╖ ╙╝╣╣╣╣╝╜ ╘╝╝╜ ╝╝╝ ╝╝╝ ╙╣╣╣ ╟╣╣╣
╩╬╬╬╬╬╬╦╦╬╬╣╣╗╣╣╣╣╣╣╣╝ ╫╣╣╣╣
╙╬╬╬╬╬╬╬╣╣╣╣╣╣╝╜
╙╬╬╬╣╣╣╜
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Version information:
ml-agents: 0.28.0,
ml-agents-envs: 0.28.0,
Communicator API: 1.5.0,
PyTorch: 1.11.0+cu115
[INFO] Listening on port 5004. Start training by pressing the Play button in the Unity Editor.
[INFO] Connected to Unity environment with package version 2.0.1 and communication version 1.5.0
[INFO] Connected new brain: WarshAgent?team=0
[WARNING] Behavior name WarshAgent does not match any behaviors specified in the trainer configuration file. A default configuration will be used.
[WARNING] Deleting TensorBoard data events.out.tfevents.1666961496.DESKTOP-EL954HL.5368.0 that was left over from a previous run.
[INFO] Hyperparameters for behavior name WarshAgent:
trainer_type: ppo
hyperparameters:
batch_size: 1024
buffer_size: 10240
learning_rate: 0.0003
beta: 0.005
epsilon: 0.2
lambd: 0.95
num_epoch: 3
learning_rate_schedule: linear
beta_schedule: linear
epsilon_schedule: linear
network_settings:
normalize: False
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: 64
summary_freq: 50000
threaded: False
self_play: None
behavioral_cloning: None
D:\Projects\Games\Warsh\venv\lib\site-packages\mlagents\trainers\torch\networks.py:91: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ..\torch\csrc\utils\tensor_new.cpp:210.)
enc.update_normalization(torch.as_tensor(vec_input))
[WARNING] Restarting worker[0] after 'Communicator has exited.'
[INFO] Listening on port 5004. Start training by pressing the Play button in the Unity Editor.
[INFO] Exported results\ppo\WarshAgent\WarshAgent-2076.onnx
[INFO] Copied results\ppo\WarshAgent\WarshAgent-2076.onnx to results\ppo\WarshAgent.onnx.
Traceback (most recent call last):
File "C:\Users\Rabiie\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\Rabiie\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 86, in _run_code
exec(code, run_globals)
File "D:\Projects\Games\Warsh\venv\Scripts\mlagents-learn.exe\__main__.py", line 7, in <module>
File "D:\Projects\Games\Warsh\venv\lib\site-packages\mlagents\trainers\learn.py", line 260, in main
run_cli(parse_command_line())
File "D:\Projects\Games\Warsh\venv\lib\site-packages\mlagents\trainers\learn.py", line 256, in run_cli
run_training(run_seed, options, num_areas)
File "D:\Projects\Games\Warsh\venv\lib\site-packages\mlagents\trainers\learn.py", line 132, in run_training
tc.start_learning(env_manager)
File "D:\Projects\Games\Warsh\venv\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped
return func(*args, **kwargs)
File "D:\Projects\Games\Warsh\venv\lib\site-packages\mlagents\trainers\trainer_controller.py", line 176, in start_learning
n_steps = self.advance(env_manager)
File "D:\Projects\Games\Warsh\venv\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped
return func(*args, **kwargs)
File "D:\Projects\Games\Warsh\venv\lib\site-packages\mlagents\trainers\trainer_controller.py", line 234, in advance
new_step_infos = env_manager.get_steps()
File "D:\Projects\Games\Warsh\venv\lib\site-packages\mlagents\trainers\env_manager.py", line 124, in get_steps
new_step_infos = self._step()
File "D:\Projects\Games\Warsh\venv\lib\site-packages\mlagents\trainers\subprocess_env_manager.py", line 420, in _step
self._restart_failed_workers(step)
File "D:\Projects\Games\Warsh\venv\lib\site-packages\mlagents\trainers\subprocess_env_manager.py", line 328, in _restart_failed_workers
self.reset(self.env_parameters)
File "D:\Projects\Games\Warsh\venv\lib\site-packages\mlagents\trainers\env_manager.py", line 68, in reset
self.first_step_infos = self._reset_env(config)
File "D:\Projects\Games\Warsh\venv\lib\site-packages\mlagents\trainers\subprocess_env_manager.py", line 446, in _reset_env
ew.previous_step = EnvironmentStep(ew.recv().payload, ew.worker_id, {}, {})
File "D:\Projects\Games\Warsh\venv\lib\site-packages\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.

(venv) D:\Projects\Games\Warsh>

What am I doing wrong exactly?