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Unity-Technologies / ml-agents

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
https://unity.com/products/machine-learning-agents
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ML agents error #6098

Closed Giainus9 closed 2 months ago

Giainus9 commented 2 months ago

I'm creating this video game that has artificial intelligence as enemies. I had just started the training session and everything started fine, about 10 seconds later it gave me this error:

C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents\trainers\torch_entities\utils.py:289: UserWarning: The use of x.T on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider x.mT to transpose batches of matrices or x.permute(*torch.arange(x.ndim - 1, -1, -1)) to reverse the dimensions of a tensor. (Triggered internally at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\TensorShape.cpp:3641.) torch.nn.functional.one_hot(_act.T, action_size[i]).float() C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\torch\onnx\symbolic_opset9.py:4662: UserWarning: Exporting a model to ONNX with a batch_size other than 1, with a variable length with LSTM can cause an error when running the ONNX model with a different batch size. Make sure to save the model with a batch size of 1, or define the initial states (h0/c0) as inputs of the model. warnings.warn( [INFO] Exported results\=Jolleen2\Jolleen\Jolleen-1152.onnx [INFO] Copied results\=Jolleen2\Jolleen\Jolleen-1152.onnx to results\=Jolleen2\Jolleen.onnx. [INFO] Exported results\=Jolleen2\TrainingPlayer\TrainingPlayer-192.onnx [INFO] Copied results\=Jolleen2\TrainingPlayer\TrainingPlayer-192.onnx to results\=Jolleen2\TrainingPlayer.onnx. Traceback (most recent call last): File "C:\Users\Utente\AppData\Local\Programs\Python\Python39\lib\runpy.py", line 197, in _run_module_as_main return _run_code(code, main_globals, None, File "C:\Users\Utente\AppData\Local\Programs\Python\Python39\lib\runpy.py", line 87, in _run_code exec(code, run_globals) File "C:\Users\Utente\Fight For Life\MLvenv\Scripts\mlagents-learn.exe__main__.py", line 7, in File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents\trainers\learn.py", line 264, in main run_cli(parse_command_line()) File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents\trainers\learn.py", line 260, in run_cli run_training(run_seed, options, num_areas) File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents\trainers\learn.py", line 136, in run_training tc.start_learning(env_manager) File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped return func(*args, kwargs) File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents\trainers\trainer_controller.py", line 175, in start_learning n_steps = self.advance(env_manager) File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped return func(*args, *kwargs) File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents\trainers\trainer_controller.py", line 250, in advance trainer.advance() File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents\trainers\ghost\trainer.py", line 254, in advance self.trainer.advance() File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents\trainers\trainer\rl_trainer.py", line 302, in advance if self._update_policy(): File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped return func(args, kwargs) File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents\trainers\trainer\off_policy_trainer.py", line 211, in _update_policy update_stats = self.optimizer.update(sampled_minibatch, n_sequences) File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped return func(*args, **kwargs) File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents\trainers\sac\optimizer_torch.py", line 573, in update q1_stream = self._condense_q_streams(q1_out, disc_actions) File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents\trainers\sac\optimizer_torch.py", line 467, in _condense_q_streams branched_q = ModelUtils.break_into_branches( File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents\trainers\torch_entities\utils.py", line 270, in break_into_branches branched_logits = [ File "C:\Users\Utente\Fight For Life\MLvenv\lib\site-packages\mlagents\trainers\torch_entities\utils.py", line 271, in concatenated_logits[:, action_idx[i] : action_idx[i + 1]] IndexError: too many indices for tensor of dimension 1