# Observation\_space not provided in PolicySpec **URL:** \ **Category:** RLlib\ **Created:** [June 14, 2022, 2:25pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501 "2022-06-14T14:25:02Z")\ **Posts on this page:** 20\ **Page:** 1
**Author:** ![evo11x](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/evo11x/32/2303_2.png) [@evo11x](https://discuss.ray.io/u/evo11x)\ **Post date:** [June 14, 2022, 2:25pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/1 "2022-06-14T14:25:02Z")
I don’t know why I am getting this error when running Tune with IMPALA with single agent custom env, if I run the trainer without tune, it runs for a few minutes then it crashes. ValueError: `observation_space` not provided in PolicySpec for default\_policy and env does not have an observation space OR no spaces received from other workers’ env(s) OR no `observation_space` specified in config! I am using Python 3.9, Windows 11 (on linux I get the same error), ray 1.13.0 Hardware AMD 16-core, 128GB RAM, RTX 3060 12GB cfg = impala.DEFAULT\_CONFIG.copy() cfg[“num\_gpus”] = 1 cfg[“num\_workers”] = 10 cfg[“num\_envs\_per\_worker”] = 10 cfg[“framework”] = “torch” cfg[“horizon”] = 750 cfg[“model”] = {“fcnet\_hiddens”: [512, 512],} tune.run(“IMPALA”, “run”, config=cfg, verbose=1) - High: It blocks me to complete my task. ---
**Author:** ![Peter\_Pirog](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/peter_pirog/32/234_2.png) [@Peter\_Pirog](https://discuss.ray.io/u/Peter_Pirog)\ **Post date:** [June 14, 2022, 2:48pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/2 "2022-06-14T14:48:37Z")
@evoyan you can add “observation\_space” in **init** method of your environment. Here is the page [https://alexandervandekleut.github.io/gym-wrappers/](https://alexandervandekleut.github.io/gym-wrappers/) how to use wrapper. You can define observation space with the Box, **of course You have to define proper shape** : ```auto self.observation_space = Box(0, 2, (self.max_avail_actions,), ) ``` I hope it will help You. ---
**Author:** ![evo11x](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/evo11x/32/2303_2.png) [@evo11x](https://discuss.ray.io/u/evo11x)\ **Post date:** [June 14, 2022, 2:55pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/3 "2022-06-14T14:55:31Z")
I already have an observation\_space in init self.observation\_space = spaces.Box(low=-np.inf, high=np.inf, shape=(42410,), dtype=np.float32) ---
**Author:** ![Peter\_Pirog](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/peter_pirog/32/234_2.png) [@Peter\_Pirog](https://discuss.ray.io/u/Peter_Pirog)\ **Post date:** [June 14, 2022, 2:58pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/4 "2022-06-14T14:58:42Z")
You can check if your observations have type np.float32 ---
**Author:** ![evo11x](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/evo11x/32/2303_2.png) [@evo11x](https://discuss.ray.io/u/evo11x)\ **Post date:** [June 14, 2022, 3:10pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/5 "2022-06-14T15:10:31Z")
This is how I prepare the data for obs, I hope it’s ok obs = np.concatenate((self.data1[self.index], self.data2 ), axis=0).astype(np.float32) return obs ---
**Author:** ![evo11x](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/evo11x/32/2303_2.png) [@evo11x](https://discuss.ray.io/u/evo11x)\ **Post date:** [June 14, 2022, 3:29pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/6 "2022-06-14T15:29:00Z")
my environment is inherited from gym.Env instead of gym.Wrapper, is that a problem? ---
**Author:** ![Peter\_Pirog](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/peter_pirog/32/234_2.png) [@Peter\_Pirog](https://discuss.ray.io/u/Peter_Pirog)\ **Post date:** [June 14, 2022, 3:37pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/7 "2022-06-14T15:37:11Z")
You can add observation space in Your configuration: [https://docs.ray.io/en/latest/rllib/rllib-training.html#common-parameters](https://docs.ray.io/en/latest/rllib/rllib-training.html#common-parameters) Yoy can try with parameter: ` "observation_space": None,` ---
**Author:** ![evo11x](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/evo11x/32/2303_2.png) [@evo11x](https://discuss.ray.io/u/evo11x)\ **Post date:** [June 14, 2022, 3:40pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/8 "2022-06-14T15:40:27Z")
I have added cfg[“observation\_space”] = None but I get the same error (I assume that None is default) ---
**Author:** ![Peter\_Pirog](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/peter_pirog/32/234_2.png) [@Peter\_Pirog](https://discuss.ray.io/u/Peter_Pirog)\ **Post date:** [June 14, 2022, 3:43pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/9 "2022-06-14T15:43:07Z")
Can you copy and paste error log? ---
**Author:** ![evo11x](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/evo11x/32/2303_2.png) [@evo11x](https://discuss.ray.io/u/evo11x)\ **Post date:** [June 14, 2022, 3:50pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/10 "2022-06-14T15:50:21Z")
Here it is a log I tried with PPO and 1 worker/1 env, but got the same error ```auto 2022-06-14 18:45:05,932 ERROR syncer.py:147 -- Log sync requires rsync to be installed. (PPOTrainer pid=30456) 2022-06-14 18:45:09,680 INFO ppo.py:414 -- In multi-agent mode, policies will be optimized sequentially by the multi-GPU optimizer. Consider setting simple_optimizer=True if this doesn't work for you. (PPOTrainer pid=30456) 2022-06-14 18:45:09,680 INFO trainer.py:903 -- Current log_level is WARN. For more information, set 'log_level': 'INFO' / 'DEBUG' or use the -v and -vv flags. == Status == Current time: 2022-06-14 18:45:13 (running for 00:00:07.80) Memory usage on this node: 15.9/95.9 GiB Using FIFO scheduling algorithm. Resources requested: 2.0/32 CPUs, 1.0/1 GPUs, 0.0/49.48 GiB heap, 0.0/24.74 GiB objects Result logdir: C:\Users\m1\ray_results\run Number of trials: 1/1 (1 RUNNING) 2022-06-14 18:45:13,587 ERROR trial_runner.py:886 -- Trial PPO_None_f6442_00000: Error processing event. NoneType: None == Status == Current time: 2022-06-14 18:45:13 (running for 00:00:07.80) Memory usage on this node: 15.9/95.9 GiB Using FIFO scheduling algorithm. Resources requested: 0/32 CPUs, 0/1 GPUs, 0.0/49.48 GiB heap, 0.0/24.74 GiB objects Result logdir: C:\Users\m1\ray_results\run Number of trials: 1/1 (1 ERROR) Number of errored trials: 1 +----------------------+--------------+-----------------------------------------------------------------------------------+ | Trial name | # failures | error file | |----------------------+--------------+-----------------------------------------------------------------------------------| | PPO_None_f6442_00000 | 1 | C:\Users\m1\ray_results\run\PPO_None_f6442_00000_0_2022-06-14_18-45-05\error.txt | +----------------------+--------------+-----------------------------------------------------------------------------------+ == Status == Current time: 2022-06-14 18:45:13 (running for 00:00:07.80) Memory usage on this node: 15.9/95.9 GiB Using FIFO scheduling algorithm. Resources requested: 0/32 CPUs, 0/1 GPUs, 0.0/49.48 GiB heap, 0.0/24.74 GiB objects Result logdir: C:\Users\m1\ray_results\run Number of trials: 1/1 (1 ERROR) Number of errored trials: 1 +----------------------+--------------+-----------------------------------------------------------------------------------+ | Trial name | # failures | error file | |----------------------+--------------+-----------------------------------------------------------------------------------| | PPO_None_f6442_00000 | 1 | C:\Users\m1\ray_results\run\PPO_None_f6442_00000_0_2022-06-14_18-45-05\error.txt | +----------------------+--------------+-----------------------------------------------------------------------------------+ 2022-06-14 18:45:13,594 ERROR ray_trial_executor.py:107 -- An exception occurred when trying to stop the Ray actor:Traceback (most recent call last): File "D:\Proj\.env\lib\site-packages\ray\tune\ray_trial_executor.py", line 98, in post_stop_cleanup ray.get(future, timeout=0) File "D:\Proj\.env\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "D:\Proj\.env\lib\site-packages\ray\worker.py", line 1833, in get raise value ray.exceptions.RayActorError: The actor died because of an error raised in its creation task, ray::PPOTrainer. __init__ () (pid=30456, ip=127.0.0.1, repr=PPOTrainer) File "D:\Proj\.env\lib\site-packages\ray\util\tracing\tracing_helper.py", line 462, in _resume_span return method(self, *_args, **_kwargs) File "D:\Proj\.env\lib\site-packages\ray\rllib\agents\trainer.py", line 1074, in _init raise NotImplementedError NotImplementedError During handling of the above exception, another exception occurred: ray::PPOTrainer. __init__ () (pid=30456, ip=127.0.0.1, repr=PPOTrainer) File "python\ray\_raylet.pyx", line 658, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 699, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 665, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 669, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 616, in ray._raylet.execute_task.function_executor File "D:\Proj\.env\lib\site-packages\ray\_private\function_manager.py", line 675, in actor_method_executor return method(__ray_actor, *args, **kwargs) File "D:\Proj\.env\lib\site-packages\ray\util\tracing\tracing_helper.py", line 462, in _resume_span return method(self, *_args, **_kwargs) File "D:\Proj\.env\lib\site-packages\ray\rllib\agents\trainer.py", line 870, in __init__ super(). __init__ ( File "D:\Proj\.env\lib\site-packages\ray\tune\trainable.py", line 156, in __init__ self.setup(copy.deepcopy(self.config)) File "D:\Proj\.env\lib\site-packages\ray\util\tracing\tracing_helper.py", line 462, in _resume_span return method(self, *_args, **_kwargs) File "D:\Proj\.env\lib\site-packages\ray\rllib\agents\trainer.py", line 950, in setup self.workers = WorkerSet( File "D:\Proj\.env\lib\site-packages\ray\rllib\evaluation\worker_set.py", line 142, in __init__ remote_spaces = ray.get( File "D:\Proj\.env\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "D:\Proj\.env\lib\site-packages\ray\worker.py", line 1833, in get raise value ray.exceptions.RayActorError: The actor died because of an error raised in its creation task, ray::RolloutWorker. __init__ () (pid=31592, ip=127.0.0.1, repr=) File "python\ray\_raylet.pyx", line 665, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 669, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 616, in ray._raylet.execute_task.function_executor File "D:\Proj\.env\lib\site-packages\ray\_private\function_manager.py", line 675, in actor_method_executor return method(__ray_actor, *args, **kwargs) File "D:\Proj\.env\lib\site-packages\ray\util\tracing\tracing_helper.py", line 462, in _resume_span return method(self, *_args, **_kwargs) File "D:\Proj\.env\lib\site-packages\ray\rllib\evaluation\rollout_worker.py", line 573, in __init__ self.policy_dict = _determine_spaces_for_multi_agent_dict( File "D:\Proj\.env\lib\site-packages\ray\rllib\evaluation\rollout_worker.py", line 1937, in _determine_spaces_for_multi_agent_dict raise ValueError( ValueError: `observation_space` not provided in PolicySpec for default_policy and env does not have an observation space OR no spaces received from other workers' env(s) OR no `observation_space` specified in config! (PPOTrainer pid=30456) 2022-06-14 18:45:13,583 ERROR worker.py:451 -- Exception raised in creation task: The actor died because of an error raised in its creation task, ray::PPOTrainer. __init__ () (pid=30456, ip=127.0.0.1, repr=PPOTrainer) (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\util\tracing\tracing_helper.py", line 462, in _resume_span (PPOTrainer pid=30456) return method(self, *_args, **_kwargs) (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\rllib\agents\trainer.py", line 1074, in _init (PPOTrainer pid=30456) raise NotImplementedError (PPOTrainer pid=30456) NotImplementedError (PPOTrainer pid=30456) (PPOTrainer pid=30456) During handling of the above exception, another exception occurred: (PPOTrainer pid=30456) (PPOTrainer pid=30456) ray::PPOTrainer. __init__ () (pid=30456, ip=127.0.0.1, repr=PPOTrainer) (PPOTrainer pid=30456) File "python\ray\_raylet.pyx", line 658, in ray._raylet.execute_task (PPOTrainer pid=30456) File "python\ray\_raylet.pyx", line 699, in ray._raylet.execute_task (PPOTrainer pid=30456) File "python\ray\_raylet.pyx", line 665, in ray._raylet.execute_task (PPOTrainer pid=30456) File "python\ray\_raylet.pyx", line 669, in ray._raylet.execute_task (PPOTrainer pid=30456) File "python\ray\_raylet.pyx", line 616, in ray._raylet.execute_task.function_executor (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\_private\function_manager.py", line 675, in actor_method_executor (PPOTrainer pid=30456) return method(__ray_actor, *args, **kwargs) (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\util\tracing\tracing_helper.py", line 462, in _resume_span (PPOTrainer pid=30456) return method(self, *_args, **_kwargs) (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\rllib\agents\trainer.py", line 870, in __init__ (PPOTrainer pid=30456) super(). __init__ ( (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\tune\trainable.py", line 156, in __init__ (PPOTrainer pid=30456) self.setup(copy.deepcopy(self.config)) (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\util\tracing\tracing_helper.py", line 462, in _resume_span (PPOTrainer pid=30456) return method(self, *_args, **_kwargs) (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\rllib\agents\trainer.py", line 950, in setup (PPOTrainer pid=30456) self.workers = WorkerSet( (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\rllib\evaluation\worker_set.py", line 142, in __init__ (PPOTrainer pid=30456) remote_spaces = ray.get( (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper (PPOTrainer pid=30456) return func(*args, **kwargs) (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\worker.py", line 1833, in get Traceback (most recent call last): File "D:\Proj\kub_IMPALA.py", line 93, in (PPOTrainer pid=30456) raise value result = tune.run("PPO", "run", config=cfg, verbose=1) File "D:\Proj\.env\lib\site-packages\ray\tune\tune.py", line 741, in run (PPOTrainer pid=30456) ray.exceptions.RayActorError: The actor died because of an error raised in its creation task, raise TuneError("Trials did not complete", incomplete_trials) ray::RolloutWorker. __init__ ()ray.tune.error. (pid=31592, ip=127.0.0.1, repr=)TuneError : ('Trials did not complete', [PPO_None_f6442_00000]) (PPOTrainer pid=30456) File "python\ray\_raylet.pyx", line 665, in ray._raylet.execute_task (PPOTrainer pid=30456) File "python\ray\_raylet.pyx", line 669, in ray._raylet.execute_task (PPOTrainer pid=30456) File "python\ray\_raylet.pyx", line 616, in ray._raylet.execute_task.function_executor (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\_private\function_manager.py", line 675, in actor_method_executor (PPOTrainer pid=30456) return method(__ray_actor, *args, **kwargs) (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\util\tracing\tracing_helper.py", line 462, in _resume_span (PPOTrainer pid=30456) return method(self, *_args, **_kwargs) (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\rllib\evaluation\rollout_worker.py", line 573, in __init__ (PPOTrainer pid=30456) self.policy_dict = _determine_spaces_for_multi_agent_dict( (PPOTrainer pid=30456) File "D:\Proj\.env\lib\site-packages\ray\rllib\evaluation\rollout_worker.py", line 1937, in _determine_spaces_for_multi_agent_dict (PPOTrainer pid=30456) raise ValueError( (PPOTrainer pid=30456) ValueError: `observation_space` not provided in PolicySpec for default_policy and env does not have an observation space OR no spaces received from other workers' env(s) OR no `observation_space` specified in config! (RolloutWorker pid=31592) 2022-06-14 18:45:13,577 ERROR worker.py:451 -- Exception raised in creation task: The actor died because of an error raised in its creation task, ray::RolloutWorker. __init__ () (pid=31592, ip=127.0.0.1, repr=) (RolloutWorker pid=31592) File "python\ray\_raylet.pyx", line 665, in ray._raylet.execute_task (RolloutWorker pid=31592) File "python\ray\_raylet.pyx", line 669, in ray._raylet.execute_task (RolloutWorker pid=31592) File "python\ray\_raylet.pyx", line 616, in ray._raylet.execute_task.function_executor (RolloutWorker pid=31592) File "D:\Proj\.env\lib\site-packages\ray\_private\function_manager.py", line 675, in actor_method_executor (RolloutWorker pid=31592) return method(__ray_actor, *args, **kwargs) (RolloutWorker pid=31592) File "D:\Proj\.env\lib\site-packages\ray\util\tracing\tracing_helper.py", line 462, in _resume_span (RolloutWorker pid=31592) return method(self, *_args, **_kwargs) (RolloutWorker pid=31592) File "D:\Proj\.env\lib\site-packages\ray\rllib\evaluation\rollout_worker.py", line 573, in __init__ (RolloutWorker pid=31592) self.policy_dict = _determine_spaces_for_multi_agent_dict( (RolloutWorker pid=31592) File "D:\Proj\.env\lib\site-packages\ray\rllib\evaluation\rollout_worker.py", line 1937, in _determine_spaces_for_multi_agent_dict (RolloutWorker pid=31592) raise ValueError( (RolloutWorker pid=31592) ValueError: `observation_space` not provided in PolicySpec for default_policy and env does not have an observation space OR no spaces received from other workers' env(s) OR no `observation_space` specified in config! (pid=) 2022-06-14 18:45:14,113 INFO context.py:67 -- Exec'ing worker with command: "D:\Proj\.env\Scripts\python.exe" D:\Proj\.env\lib\site-packages\ray\workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=57762 --object-store-name=tcp://127.0.0.1:64785 --raylet-name=tcp://127.0.0.1:58844 --redis-address=None --storage=None --temp-dir=C:\Users\m1\AppData\Local\Temp\ray --metrics-agent-port=59156 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:59770 --redis-password=5241590000000000 --startup-token=32 --runtime-env-hash=185949076 ``` ---
**Author:** ![evo11x](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/evo11x/32/2303_2.png) [@evo11x](https://discuss.ray.io/u/evo11x)\ **Post date:** [June 14, 2022, 3:56pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/11 "2022-06-14T15:56:13Z")
What is strange that the environment is working, with trainer = impala.ImpalaTrainer(env=“my\_env”, config=cfg) and fewer workers I was able to get about 7mil episodes, running ray 1.9.2, after I have upgraded to ray 1.11.0 to 1.13.0 it started to crash sooner and with tune it seems that it does not run at all. ---
**Author:** ![Peter\_Pirog](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/peter_pirog/32/234_2.png) [@Peter\_Pirog](https://discuss.ray.io/u/Peter_Pirog)\ **Post date:** [June 14, 2022, 4:04pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/12 "2022-06-14T16:04:44Z")
maybe try config: ```auto cfg[“observation_space”]=spaces.Box(low=-np.inf, high=np.inf, shape=(42410,), dtype=np.float32) ``` You can change np.inf to some definer value ---
**Author:** ![evo11x](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/evo11x/32/2303_2.png) [@evo11x](https://discuss.ray.io/u/evo11x)\ **Post date:** [June 14, 2022, 4:11pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/13 "2022-06-14T16:11:50Z")
I get the same error with cfg and also with np.inf replaced with value ---
**Author:** ![Peter\_Pirog](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/peter_pirog/32/234_2.png) [@Peter\_Pirog](https://discuss.ray.io/u/Peter_Pirog)\ **Post date:** [June 14, 2022, 4:21pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/14 "2022-06-14T16:21:24Z")
I have no new ideas now 😐 ---
**Author:** ![evo11x](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/evo11x/32/2303_2.png) [@evo11x](https://discuss.ray.io/u/evo11x)\ **Post date:** [June 14, 2022, 5:27pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/15 "2022-06-14T17:27:47Z")
Here is an empty environment with which I get the same error ```auto import random import gym from gym import spaces import numpy as np import random class MyEnv(gym.Env): def __init__ (self, config=None): super(MyEnv, self). __init__ () self.action_space = spaces.Box( low=-1, high=1, shape=(2,), dtype=np.float32) self.observation_space = spaces.Box(low=-np.inf, high=np.inf, shape=(42410,), dtype=np.float32) def _next_observation(self): obs = np.random.rand(42410) return obs def _take_action(self, action): self._reward = 1 def step(self, action): self._reward = 0 self._take_action(action) done = False obs = self._next_observation() return obs, self._reward, done, {} def reset(self): self._reward = 0 self.total_reward = 0 self.visualization = None return self._next_observation() ``` ---
**Author:** ![mannyv](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/mannyv/32/606_2.png) [@mannyv](https://discuss.ray.io/u/mannyv)\ **Post date:** [June 14, 2022, 6:06pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/16 "2022-06-14T18:06:22Z")
@evo11x, There must be another detail of your setup that is causing the error. Here is a colab link showing a very minimal example of the environment you provided running fine. Perhaps you could try to add your other customizations and see which breaks it. > **[Google Colaboratory](https://colab.research.google.com/drive/19aZ_bUFJwJuPT4dRzVO2cTn8Ey0G7CoA?usp=sharing)** ---
**Author:** ![Peter\_Pirog](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/peter_pirog/32/234_2.png) [@Peter\_Pirog](https://discuss.ray.io/u/Peter_Pirog)\ **Post date:** [June 14, 2022, 6:14pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/17 "2022-06-14T18:14:11Z")
I changed to gpu, I dont’t have any error. ```auto 022-06-14 18:09:51,271 INFO services.py:1476 -- View the Ray dashboard at http://127.0.0.1:8265 (PPOTrainer pid=431) 2022-06-14 18:10:04,122 INFO trainer.py:2333 -- Your framework setting is 'tf', meaning you are using static-graph mode. Set framework='tf2' to enable eager execution with tf2.x. You may also then want to set eager_tracing=True in order to reach similar execution speed as with static-graph mode. (PPOTrainer pid=431) 2022-06-14 18:10:04,123 WARNING ppo.py:395 -- `train_batch_size` (256) cannot be achieved with your other settings (num_workers=1 num_envs_per_worker=1 rollout_fragment_length=10)! Auto-adjusting `rollout_fragment_length` to 256. (PPOTrainer pid=431) 2022-06-14 18:10:04,123 INFO ppo.py:415 -- In multi-agent mode, policies will be optimized sequentially by the multi-GPU optimizer. Consider setting simple_optimizer=True if this doesn't work for you. (PPOTrainer pid=431) 2022-06-14 18:10:04,123 INFO trainer.py:906 -- Current log_level is WARN. For more information, set 'log_level': 'INFO' / 'DEBUG' or use the -v and -vv flags. (RolloutWorker pid=474) 2022-06-14 18:10:08,937 WARNING env.py:136 -- Your env doesn't have a .spec.max_episode_steps attribute. This is fine if you have set 'horizon' in your config dictionary, or `soft_horizon`. However, if you haven't, 'horizon' will default to infinity, and your environment will not be reset. (PPOTrainer pid=431) 2022-06-14 18:10:16,952 INFO trainable.py:163 -- Trainable.setup took 12.831 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads. (PPOTrainer pid=431) 2022-06-14 18:10:16,952 WARNING util.py:65 -- Install gputil for GPU system monitoring. == Status == Current time: 2022-06-14 18:10:16 (running for 00:00:17.93) Memory usage on this node: 2.8/12.7 GiB Using FIFO scheduling algorithm. Resources requested: 2.0/2 CPUs, 0/1 GPUs, 0.0/7.3 GiB heap, 0.0/3.65 GiB objects (0.0/1.0 accelerator_type:T4) Result logdir: /root/ray_results/PPO Number of trials: 1/1 (1 RUNNING) Trial name status loc PPO_MyEnv_33da2_00000 RUNNING 172.28.0.2:431 (PPOTrainer pid=431) 2022-06-14 18:10:19,257 WARNING deprecation.py:47 -- DeprecationWarning: `slice` has been deprecated. Use `SampleBatch[start:stop]` instead. This will raise an error in the future! == Status == Current time: 2022-06-14 18:10:22 (running for 00:00:22.97) Memory usage on this node: 3.0/12.7 GiB Using FIFO scheduling algorithm. Resources requested: 2.0/2 CPUs, 0/1 GPUs, 0.0/7.3 GiB heap, 0.0/3.65 GiB objects (0.0/1.0 accelerator_type:T4) Result logdir: /root/ray_results/PPO Number of trials: 1/1 (1 RUNNING) Trial name status loc PPO_MyEnv_33da2_00000 RUNNING 172.28.0.2:431 == Status == Current time: 2022-06-14 18:10:27 (running for 00:00:27.99) Memory usage on this node: 3.0/12.7 GiB Using FIFO scheduling algorithm. Resources requested: 2.0/2 CPUs, 0/1 GPUs, 0.0/7.3 GiB heap, 0.0/3.65 GiB objects (0.0/1.0 accelerator_type:T4) Result logdir: /root/ray_results/PPO Number of trials: 1/1 (1 RUNNING) Trial name status loc PPO_MyEnv_33da2_00000 RUNNING 172.28.0.2:431 == Status == Current time: 2022-06-14 18:10:32 (running for 00:00:33.04) Memory usage on this node: 3.0/12.7 GiB Using FIFO scheduling algorithm. Resources requested: 2.0/2 CPUs, 0/1 GPUs, 0.0/7.3 GiB heap, 0.0/3.65 GiB objects (0.0/1.0 accelerator_type:T4) Result logdir: /root/ray_results/PPO Number of trials: 1/1 (1 RUNNING) Trial name status loc PPO_MyEnv_33da2_00000 RUNNING 172.28.0.2:431 Result for PPO_MyEnv_33da2_00000: agent_timesteps_total: 256 counters: num_agent_steps_sampled: 256 num_agent_steps_trained: 256 num_env_steps_sampled: 256 num_env_steps_trained: 256 custom_metrics: {} date: 2022-06-14_18-10-34 done: true episode_len_mean: 10.0 episode_media: {} episode_reward_max: 10.0 episode_reward_mean: 10.0 episode_reward_min: 10.0 episodes_this_iter: 25 episodes_total: 25 experiment_id: 7d49449601d54b969a05203fde2f9b11 hostname: dda9382a7eae info: learner: default_policy: custom_metrics: {} learner_stats: cur_kl_coeff: 0.20000000298023224 cur_lr: 4.999999873689376e-05 entropy: 2.761671781539917 entropy_coeff: 0.0 kl: 0.015223202295601368 model: {} policy_loss: -0.1377173811197281 total_loss: 7.826196193695068 vf_explained_var: -8.736054041946772e-06 vf_loss: 7.960869312286377 num_agent_steps_trained: 128.0 num_agent_steps_sampled: 256 num_agent_steps_trained: 256 num_env_steps_sampled: 256 num_env_steps_trained: 256 iterations_since_restore: 1 node_ip: 172.28.0.2 num_agent_steps_sampled: 256 num_agent_steps_trained: 256 num_env_steps_sampled: 256 num_env_steps_sampled_this_iter: 256 num_env_steps_trained: 256 num_env_steps_trained_this_iter: 256 num_healthy_workers: 1 off_policy_estimator: {} perf: cpu_util_percent: 94.98076923076925 ram_util_percent: 23.4 pid: 431 policy_reward_max: {} policy_reward_mean: {} policy_reward_min: {} sampler_perf: mean_action_processing_ms: 0.15440618017768118 mean_env_render_ms: 0.0 mean_env_wait_ms: 0.4018957976701195 mean_inference_ms: 7.579902730563272 mean_raw_obs_processing_ms: 0.4254426473773407 sampler_results: custom_metrics: {} episode_len_mean: 10.0 episode_media: {} episode_reward_max: 10.0 episode_reward_mean: 10.0 episode_reward_min: 10.0 episodes_this_iter: 25 hist_stats: episode_lengths: - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 - 10 episode_reward: - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 - 10.0 off_policy_estimator: {} policy_reward_max: {} policy_reward_mean: {} policy_reward_min: {} sampler_perf: mean_action_processing_ms: 0.15440618017768118 mean_env_render_ms: 0.0 mean_env_wait_ms: 0.4018957976701195 mean_inference_ms: 7.579902730563272 mean_raw_obs_processing_ms: 0.4254426473773407 time_since_restore: 17.989404678344727 time_this_iter_s: 17.989404678344727 time_total_s: 17.989404678344727 timers: learn_throughput: 16.417 learn_time_ms: 15593.118 load_throughput: 984181.324 load_time_ms: 0.26 training_iteration_time_ms: 17952.205 update_time_ms: 71.665 timestamp: 1655230234 timesteps_since_restore: 0 timesteps_total: 256 training_iteration: 1 trial_id: 33da2_00000 warmup_time: 12.848384857177734 == Status == Current time: 2022-06-14 18:10:35 (running for 00:00:36.03) Memory usage on this node: 3.1/12.7 GiB Using FIFO scheduling algorithm. Resources requested: 0/2 CPUs, 0/1 GPUs, 0.0/7.3 GiB heap, 0.0/3.65 GiB objects (0.0/1.0 accelerator_type:T4) Result logdir: /root/ray_results/PPO Number of trials: 1/1 (1 TERMINATED) Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean PPO_MyEnv_33da2_00000 TERMINATED 172.28.0.2:431 1 17.9894 256 10 10 10 10 2022-06-14 18:10:35,366 INFO tune.py:748 -- Total run time: 41.70 seconds (35.97 seconds for the tuning loop). ``` ---
**Author:** ![evo11x](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/evo11x/32/2303_2.png) [@evo11x](https://discuss.ray.io/u/evo11x)\ **Post date:** [June 14, 2022, 7:34pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/18 "2022-06-14T19:34:30Z")
It seems that the problem was my env registration, now I set the env in the config without registering it. This was the problem ```auto def env_creator(env_config): return MyEnv(gymconfig) register_env("my_env", env_creator) trainer = ppo.APPOTrainer(env="my_env", config=cfg) ``` I also had installed ray with ray[rllib], so I have reinstalled it with ray[all] Thank you for helping! ---
**Author:** ![mannyv](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/mannyv/32/606_2.png) [@mannyv](https://discuss.ray.io/u/mannyv)\ **Post date:** [June 14, 2022, 7:49pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/19 "2022-06-14T19:49:58Z")
@evo11x, You are welcome. The issue is `env_creator` is not using the correct argument to the environment. If you change it to the code below it will run with registered environment name. I updated the Collab to confirm. ```auto def env_creator(env_config): return MyEnv(env_config) ``` ---
**Author:** ![zhh210](https://avatars.discourse-cdn.com/v4/letter/z/ce73a5/32.png) [@zhh210](https://discuss.ray.io/u/zhh210)\ **Post date:** [October 27, 2022, 4:47pm UTC](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501/20 "2022-10-27T16:47:46Z")
I don’t see the difference between `env_config` and `gymconfig`. Looking into the initialization of `MyEnv`, `MyEnv(env_config)` does nothing with `env_config` anyway. [Next page](https://discuss.ray.io/t/observation-space-not-provided-in-policyspec/6501.md?page=2)