Hi
I am getting this error while running RLLib training on a Unity3D Env after 4000-10000 steps:
ValueError: The parameter loc has invalid values
In tower 0 on device cpu
Hi geekyneuro,
I can not find the phrase “The parameter [x] has invalid values” inside the Ray library.
Can you maybe post a reproduction script or give more detail?
Cheers 
Here is the complete error:
cat /data/afandang/exp/ray_3/ray_3/PPO_arora-v0_6ceb3_00000_0_2021-08-17_16-26-38/error.txt
Failure # 1 (occurred at 2021-08-17_17-23-46)
Traceback (most recent call last):
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/tune/trial_runner.py", line 739, in _process_trial
results = self.trial_executor.fetch_result(trial)
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py", line 729, in fetch_result
result = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/_private/client_mode_hook.py", line 82, in wrapper
return func(*args, **kwargs)
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/worker.py", line 1564, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(ValueError): ray::PPO.train() (pid=147444, ip=192.168.1.10)
File "python/ray/_raylet.pyx", line 534, in ray._raylet.execute_task
File "python/ray/_raylet.pyx", line 484, in ray._raylet.execute_task.function_executor
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/_private/function_manager.py", line 563, in actor_method_executor
return method(__ray_actor, *args, **kwargs)
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/rllib/agents/trainer.py", line 643, in train
raise e
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/rllib/agents/trainer.py", line 629, in train
result = Trainable.train(self)
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/tune/trainable.py", line 237, in train
result = self.step()
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py", line 170, in step
res = next(self.train_exec_impl)
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/util/iter.py", line 756, in __next__
return next(self.built_iterator)
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/util/iter.py", line 783, in apply_foreach
for item in it:
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/util/iter.py", line 783, in apply_foreach
for item in it:
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/util/iter.py", line 843, in apply_filter
for item in it:
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/util/iter.py", line 843, in apply_filter
for item in it:
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/util/iter.py", line 783, in apply_foreach
for item in it:
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/util/iter.py", line 783, in apply_foreach
for item in it:
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/util/iter.py", line 791, in apply_foreach
result = fn(item)
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py", line 65, in __call__
info = do_minibatch_sgd(
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/rllib/utils/sgd.py", line 119, in do_minibatch_sgd
batch_fetches = (local_worker.learn_on_batch(
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/rllib/evaluation/rollout_worker.py", line 935, in learn_on_batch
info_out[pid] = policy.learn_on_batch(batch)
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/rllib/utils/threading.py", line 21, in wrapper
return func(self, *a, **k)
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py", line 468, in learn_on_batch
grads, fetches = self.compute_gradients(postprocessed_batch)
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/rllib/policy/policy_template.py", line 335, in compute_gradients
return parent_cls.compute_gradients(self, batch)
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/rllib/utils/threading.py", line 21, in wrapper
return func(self, *a, **k)
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py", line 519, in compute_gradients
tower_outputs = self._multi_gpu_parallel_grad_calc(batches)
File "/opt/conda/envs/navsim/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py", line 867, in _multi_gpu_parallel_grad_calc
raise last_result
ValueError: The parameter loc has invalid values
In tower 0 on device cpu
The code used is
import ray.rllib.agents.ppo as ppo
config = ObjDict(ppo.DEFAULT_CONFIG.copy())
for arg in run_config:
if arg in config:
config[arg] = run_config[arg]
config["env_config"]=env_config
config["ignore_worker_failures"]=True
# TODO: Override ray's conf with some defaults from navsim
import ray
ray.shutdown()
ray.init(ignore_reinit_error=True,local_mode=True)
navsim_envs.env.AroraGymEnv.register_with_ray()
result = ray.tune.run(
ppo.PPOTrainer,
config=config,
name=run_config.run_id,
resume=run_config.resume,
local_dir=str(run_base_folder),
stop={"episodes_total": run_config.total_episodes},
checkpoint_freq=run_config.checkpoint_interval,
checkpoint_at_end=True
)
That error comes from Pytorch’s distribution class, I would assume from Normal. E.g. see here, when you provide loc and scale to the constructor, they need to satisfy the constraints defined by arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}.
This means that either loc becomes inf or nan, which can be because of instability during training or just your model outputting a large value from the logits branch (if you’re using something similar to the built in FCnet).