Hello. I am trying to use imitation learning. However when I try to enter the recorded .demo file the following error, related to torch, appears. Thanks.
RuntimeError: cannot sample n_sample > prob_dist.size(-1) samples without replacement
ERROR LOG BELOW
–‐----------------------------
Version information:
ml-agents: 0.27.0,
ml-agents-envs: 0.27.0,
Communicator API: 1.5.0,
PyTorch: 1.9.0+cu111
[INFO] Listening on port 5004. Start training by pressing the Play button in the Unity Editor.
[INFO] Connected to Unity environment with package version 2.0.0-pre.3 and communication version 1.5.0
[INFO] Connected new brain: ZARAgoal?team=1
[WARNING] Deleting TensorBoard data events.out.tfevents.1624485266.AndreasPC.18212.0 that was left over from a previous run.
[INFO] Hyperparameters for behavior name ZARAgoal:
trainer_type:   ppo
hyperparameters:
batch_size:   128
buffer_size:  2048
learning_rate:        0.0003
beta: 0.01
epsilon:      0.2
lambd:        0.95
num_epoch:    3
learning_rate_schedule:       linear
network_settings:
normalize:    False
hidden_units: 256
num_layers:   2
vis_encode_type:      simple
memory:       None
goal_conditioning_type:       hyper
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
gail:
gamma:      0.99
strength:   0.01
network_settings:
normalize:        False
hidden_units:     128
num_layers:       2
vis_encode_type:  simple
memory:   None
goal_conditioning_type:   hyper
learning_rate:      0.0003
encoding_size:      None
use_actions:        False
use_vail:   False
demo_path:  Demos/ZARAdemos/
init_path:      None
keep_checkpoints:       5
checkpoint_interval:    500000
max_steps:      100000
time_horizon:   64
summary_freq:   60000
threaded:       False
self_play:      None
behavioral_cloning:
demo_path:    Demos/ZARAdemos/
steps:        50000
strength:     1.0
samples_per_update:   0
num_epoch:    None
batch_size:   None
d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\nn\init.py:388: UserWarning: Initializing zero-element tensors is a no-op
warnings.warn(“Initializing zero-element tensors is a no-op”)
d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\nn\init.py:426: UserWarning: Initializing zero-element tensors is a no-op
warnings.warn(“Initializing zero-element tensors is a no-op”)
Traceback (most recent call last):
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\trainer_controller.py”, line 176, in start_learning
n_steps = self.advance(env_manager)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents_envs\timers.py”, line 305, in wrapped
return func(*args, **kwargs)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\trainer_controller.py”, line 234, in advance
new_step_infos = env_manager.get_steps()
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\env_manager.py”, line 124, in get_steps
new_step_infos = self._step()
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\subprocess_env_manager.py”, line 298, in _step
self._queue_steps()
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\subprocess_env_manager.py”, line 291, in _queue_steps
env_action_info = self._take_step(env_worker.previous_step)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents_envs\timers.py”, line 305, in wrapped
return func(*args, **kwargs)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\subprocess_env_manager.py”, line 429, in _take_step
all_action_info[brain_name] = self.policies[brain_name].get_action(
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\policy\torch_policy.py”, line 212, in get_action
run_out = self.evaluate(decision_requests, global_agent_ids)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents_envs\timers.py”, line 305, in wrapped
return func(*args, **kwargs)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\policy\torch_policy.py”, line 178, in evaluate
action, log_probs, entropy, memories = self.sample_actions(
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents_envs\timers.py”, line 305, in wrapped
return func(*args, **kwargs)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\policy\torch_policy.py”, line 140, in sample_actions
actions, log_probs, entropies, memories = self.actor.get_action_and_stats(
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\torch\networks.py”, line 626, in get_action_and_stats
action, log_probs, entropies = self.action_model(encoding, masks)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\nn\modules\module.py”, line 1051, in _call_impl
return forward_call(*input, **kwargs)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\torch\action_model.py”, line 194, in forward
actions = self._sample_action(dists)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\torch\action_model.py”, line 84, in _sample_action
discrete_action.append(discrete_dist.sample())
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\torch\distributions.py”, line 114, in sample
return torch.multinomial(self.probs, 1)
RuntimeError: cannot sample n_sample > prob_dist.size(-1) samples without replacement
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File “C:\Users\antre\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\antre\AppData\Local\Programs\Python\Python39\lib\runpy.py”, line 87, in run_code
exec(code, run_globals)
File "D:\Desktop\Crowds-and-ML-Agents\venv\Scripts\mlagents-learn.exe_main.py", line 7, in 
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\learn.py”, line 250, in main
run_cli(parse_command_line())
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\learn.py”, line 246, in run_cli
run_training(run_seed, options)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\learn.py”, line 125, in run_training
tc.start_learning(env_manager)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents_envs\timers.py”, line 305, in wrapped
return func(*args, **kwargs)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\trainer_controller.py”, line 201, in start_learning
self._save_models()
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents_envs\timers.py”, line 305, in wrapped
return func(*args, **kwargs)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\trainer_controller.py”, line 80, in _save_models
self.trainers[brain_name].save_model()
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\trainer\rl_trainer.py”, line 185, in save_model
model_checkpoint = self._checkpoint()
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents_envs\timers.py”, line 305, in wrapped
return func(*args, **kwargs)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\trainer\rl_trainer.py”, line 157, in checkpoint
export_path, auxillary_paths = self.model_saver.save_checkpoint(
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\model_saver\torch_model_saver.py”, line 59, in save_checkpoint
self.export(checkpoint_path, behavior_name)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\model_saver\torch_model_saver.py”, line 64, in export
self.exporter.export_policy_model(output_filepath)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\torch\model_serialization.py”, line 159, in export_policy_model
torch.onnx.export(
File "d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\onnx_init.py", line 275, in export
return utils.export(model, args, f, export_params, verbose, training,
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\onnx\utils.py”, line 88, in export
_export(model, args, f, export_params, verbose, training, input_names, output_names,
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\onnx\utils.py”, line 689, in _export
_model_to_graph(model, args, verbose, input_names,
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\onnx\utils.py”, line 458, in _model_to_graph
graph, params, torch_out, module = _create_jit_graph(model, args,
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\onnx\utils.py”, line 422, in _create_jit_graph
graph, torch_out = _trace_and_get_graph_from_model(model, args)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\onnx\utils.py”, line 373, in _trace_and_get_graph_from_model
torch.jit._get_trace_graph(model, args, strict=False, _force_outplace=False, _return_inputs_states=True)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\jit_trace.py”, line 1160, in _get_trace_graph
outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\nn\modules\module.py”, line 1051, in _call_impl
return forward_call(*input, **kwargs)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\jit_trace.py”, line 127, in forward
graph, out = torch._C._create_graph_by_tracing(
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\jit_trace.py”, line 118, in wrapper
outs.append(self.inner(*trace_inputs))
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\nn\modules\module.py”, line 1051, in _call_impl
return forward_call(*input, **kwargs)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\torch\nn\modules\module.py”, line 1039, in _slow_forward
result = self.forward(*input, **kwargs)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\torch\networks.py”, line 664, in forward
) = self.action_model.get_action_out(encoding, masks)
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\torch\action_model.py”, line 171, in get_action_out
discrete_out_list = [
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\torch\action_model.py”, line 172, in 
discrete_dist.exported_model_output()
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\torch\distributions.py”, line 136, in exported_model_output
return self.sample()
File “d:\desktop\crowds-and-ml-agents\venv\lib\site-packages\mlagents\trainers\torch\distributions.py”, line 114, in sample
return torch.multinomial(self.probs, 1)
RuntimeError: cannot sample n_sample > prob_dist.size(-1) samples without replacement