Dear author: Thanks for your sharing! I am really interested in your work! When I type "python main.py --no-cuda ", I get that error after hours of training.

Time version: 3-2022.03.19-12-45-46 is training Updates 15320, num timesteps 4902720, FPS 225 Last 10 training episodes: mean/median reward 7.9/8.0, min/max reward 7.1/8.4 The dist entropy 0.68102, the value loss 0.59049, the action loss 0.06197 The mean space ratio is 0.7919, the ratio threshold is0.946

Traceback (most recent call last): File "main.py", line 61, in main(args) File "main.py", line 56, in main trainTool.train_n_steps(envs, args, device) File "/home/Online-3D-BPP-PCT/train_tools.py", line 66, in train_n_steps selectedlogProb, selectedIdx, dist_entropy, _ = self.PCT_policy(all_nodes, normFactor = factor) File "/home/.conda/envs/Online3D/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/home/Online-3D-BPP-PCT/model.py", line 22, in forward o, p, dist_entropy, hidden, _= self.actor(items, deterministic, normFactor = normFactor, evaluate = evaluate) File "/home/.conda/envs/Online3D/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/home/Online-3D-BPP-PCT/attention_model.py", line 129, in forward valid_length = valid_length) File "/home/Online-3D-BPP-PCT/attention_model.py", line 136, in _inner log_p, mask = self._get_log_p(fixed, mask) File "/home/Online-3D-BPP-PCT/attention_model.py", line 198, in _get_log_p assert not torch.isnan(log_p).any() AssertionError

Python == 3.7.7, torch == 1.10.1, OS: Ubuntu 18.04

Dear author: Thanks for your sharing! I am really interested in your work! I'm a undergraduate student and new to reinforcement learning. When I type "python main.py --no-cuda ", I get that error quickly.

Traceback (most recent call last): File "main.py", line 61, in main(args) File "main.py", line 43, in main envs = make_vec_envs(args, './logs/runinfo', True) File "G:\graduate design\code\Online-3D-BPP-PCT\envs.py", line 112, in make_vec_envs envs = ShmemVecEnv(envs, spaces, context='fork') File "G:\graduate design\code\Online-3D-BPP-PCT\wrapper\shmem_vec_env.py", line 30, in init ctx = mp.get_context(context) File "E:\anaconda\envs\Online3D\lib\multiprocessing\context.py", line 238, in get_context return super().get_context(method) File "E:\anaconda\envs\Online3D\lib\multiprocessing\context.py", line 192, in get_context raise ValueError('cannot find context for %r' % method) from None ValueError: cannot find context for 'fork'

Thanks in advance.

Regards

Thank you very much for your selfless sharing. I noticed that you did not show the details of the network structure in the paper, but you showed your deep reinforcement learning network structure based on the ACKTR algorithm in the Online-3D-BPP-DRL(https://github.com/alexfrom0815/Online-3D-BPP-DRL) paper. Is the network structure of deep reinforcement learning the same in this PCT paper? Or do you have a suggestion for a quick way to see the structure of the PCT network? Thanks in advance for your reply. Best wishes for you!

Dear author: Thanks for your sharing! When I ran main.py, I got an error:

Traceback (most recent call last): File "E:/User002/Online-3D-BPP-PCT-main/main.py", line 6, in from model import * File "E:\User002\Online-3D-BPP-PCT-main\model.py", line 4, in from attention_model import AttentionModel File "E:\User002\Online-3D-BPP-PCT-main\attention_model.py", line 9, in class AttentionModelFixed(NamedTuple): RuntimeError: class not set defining 'AttentionModelFixed' as <class 'attention_model.AttentionModelFixed'>. Was classcell propagated to type.new?

Process finished with exit code 1

python3.8 pytorch1.9.0

Hi,

When I type "python main.py ", I get that error quickly.

Full error trace:

Traceback (most recent call last): File "main.py", line 55, in main(args) File "main.py", line 40, in main envs = make_vec_envs(args, './logs/runinfo', True) File "G:\graduate design\code\Online-3D-BPP-PCT-main\envs.py", line 108, in make_vec_envs spaces = [env.observation_space, env.action_space] File "E:\anaconda\envs\Online3D\lib\site-packages\gym\core.py", line 229, in getattr return getattr(self.env, name) AttributeError: 'PackingDiscrete' object has no attribute 'action_space'

Thanks in advance.

Regards

Dear authors, while executing the main.py on a CPU mode, i get this error : "KeyError: 'PctDiscrete-v0' ", i was wondering if you could help me with this. sincerely I'm turning the code on win10, python 3.7, gym==0.15.7, torch == 1.10.1 This is the Traceback : Traceback (most recent call last): File "C:\Users\aketfi\Anaconda3\envs\3Dpacking\lib\site-packages\gym\envs\registration.py", line 132, in spec return self.env_specs[id] KeyError: 'PctDiscrete-v0'

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "C:\Users\aketfi\Anaconda3\envs\3Dpacking\lib\multiprocessing\process.py", line 297, in _bootstrap self.run() File "C:\Users\aketfi\Anaconda3\envs\3Dpacking\lib\multiprocessing\process.py", line 99, in run self._target(*self._args, **self._kwargs) File "C:\Users\aketfi\Online-3D-BPP-PCT\wrapper\shmem_vec_env.py", line 131, in _subproc_worker env = env_fn_wrapper.x() File "C:\Users\aketfi\Online-3D-BPP-PCT\envs.py", line 46, in _thunk sample_right_bound = args.sample_right_bound File "C:\Users\aketfi\Anaconda3\envs\3Dpacking\lib\site-packages\gym\envs\registration.py", line 156, in make return registry.make(id, **kwargs) File "C:\Users\aketfi\Anaconda3\envs\3Dpacking\lib\site-packages\gym\envs\registration.py", line 100, in make spec = self.spec(path) File "C:\Users\aketfi\Anaconda3\envs\3Dpacking\lib\site-packages\gym\envs\registration.py", line 142, in spec raise error.UnregisteredEnv('No registered env with id: {}'.format(id))

Dear author, Thanks for your sharing! I found the "BPP-K" function in your paper "Online 3D Bin Packing with Constrained Deep Reinforcement Learning" is not added to the code. Is it possible to join the preview function to the code? Thanks for your sharing again!

Dear authors:

I am trying to reproduce your experiments claimed in your ICLR paper. But I got an error "You must specify a action space", the whole error information can be found below. Would you mind please providing some helpful information for this?

/home/liwj/gitcode/Online-3D-BPP-PCT/attention_model.py:9: DeprecationWarning: class not set defining 'AttentionModelFixed' as <class 'attention_model.AttentionModelFixed'>. Was classcell propagated to type.new? class AttentionModelFixed(NamedTuple): Please input the experiment name pct [(1, 1, 1), (1, 1, 2), (1, 1, 3), (1, 1, 4), (1, 1, 5), (1, 2, 1), (1, 2, 2), (1, 2, 3), (1, 2, 4), (1, 2, 5), (1, 3, 1), (1, 3, 2), (1, 3, 3), (1, 3, 4), (1, 3, 5), (1, 4, 1), (1, 4, 2), (1, 4, 3), (1, 4, 4), (1, 4, 5), (1, 5, 1), (1, 5, 2), (1, 5, 3), (1, 5, 4), (1, 5, 5), (2, 1, 1), (2, 1, 2), (2, 1, 3), (2, 1, 4), (2, 1, 5), (2, 2, 1), (2, 2, 2), (2, 2, 3), (2, 2, 4), (2, 2, 5), (2, 3, 1), (2, 3, 2), (2, 3, 3), (2, 3, 4), (2, 3, 5), (2, 4, 1), (2, 4, 2), (2, 4, 3), (2, 4, 4), (2, 4, 5), (2, 5, 1), (2, 5, 2), (2, 5, 3), (2, 5, 4), (2, 5, 5), (3, 1, 1), (3, 1, 2), (3, 1, 3), (3, 1, 4), (3, 1, 5), (3, 2, 1), (3, 2, 2), (3, 2, 3), (3, 2, 4), (3, 2, 5), (3, 3, 1), (3, 3, 2), (3, 3, 3), (3, 3, 4), (3, 3, 5), (3, 4, 1), (3, 4, 2), (3, 4, 3), (3, 4, 4), (3, 4, 5), (3, 5, 1), (3, 5, 2), (3, 5, 3), (3, 5, 4), (3, 5, 5), (4, 1, 1), (4, 1, 2), (4, 1, 3), (4, 1, 4), (4, 1, 5), (4, 2, 1), (4, 2, 2), (4, 2, 3), (4, 2, 4), (4, 2, 5), (4, 3, 1), (4, 3, 2), (4, 3, 3), (4, 3, 4), (4, 3, 5), (4, 4, 1), (4, 4, 2), (4, 4, 3), (4, 4, 4), (4, 4, 5), (4, 5, 1), (4, 5, 2), (4, 5, 3), (4, 5, 4), (4, 5, 5), (5, 1, 1), (5, 1, 2), (5, 1, 3), (5, 1, 4), (5, 1, 5), (5, 2, 1), (5, 2, 2), (5, 2, 3), (5, 2, 4), (5, 2, 5), (5, 3, 1), (5, 3, 2), (5, 3, 3), (5, 3, 4), (5, 3, 5), (5, 4, 1), (5, 4, 2), (5, 4, 3), (5, 4, 4), (5, 4, 5), (5, 5, 1), (5, 5, 2), (5, 5, 3), (5, 5, 4), (5, 5, 5)] Traceback (most recent call last): File "main.py", line 61, in main(args) File "main.py", line 43, in main envs = make_vec_envs(args, './logs/runinfo', True) File "/home/liwj/gitcode/Online-3D-BPP-PCT/envs.py", line 104, in make_vec_envs sample_right_bound=args.sample_right_bound File "/home/liwj/.conda/envs/pytorch-wenjie/lib/python3.7/site-packages/gym/envs/registration.py", line 601, in make env = PassiveEnvChecker(env) File "/home/liwj/.conda/envs/pytorch-wenjie/lib/python3.7/site-packages/gym/wrappers/env_checker.py", line 24, in init ), "You must specify a action space. https://www.gymlibrary.ml/content/environment_creation/" AssertionError: You must specify a action space. https://www.gymlibrary.ml/content/environment_creation/

Hi, excellent work on your paper and thank you so much for sharing your code! This is fascinating research! I wanted to explore your work more through the code, but I was struggling to achieve the same performance in the discrete environment and the continuous environment. I think the error is lines 160 and 164 in PCTContinuous0/bin3D.py. The break statements should be indented. This gives me the same performance between discrete and continuous :)

Thank you for your sharing, it's an incredible job! I‘ve read your paper and am interested in the real-world experiments. I found that the size of the new box can be defined by the class bincreator, but I'd love to know how you "correct the descriptor of the corresponding internal node b ∈ B with the offset position". Could you please give a specific location in the code？ Finally, I would also like to know what your LICENSE is and if I can modify it for further study and research. Thank you whatever and best wishes for you!

Hello,I have a question about whether this project is online or offline learning: as far as I understand, online learning is usually input one piece of data at a time (not one batch) , directly update the weights after training.While offline learning, similar to batch learning, updates the weights after a batch of training.Can we input one data for training at a time in the training of this project?

Thank you in advance for your reply, and best wishes to you!

Hello Great work. I am curious on the following two aspects of the work in real-world use-case.

1. Validity of the tree and leaf nodes after placing an item: In real-world scenarios, the actual pose where the robot placed the object and the pose given by the policy might differ a little because of manipulation errors and noise. Additionally, there's also a chance that the current object being placed might move or disturb the previously placed objects. In these scenarios, some of the existing nodes in the tree might not be valid(as corner points might have moved by small amount) and so the whole tree structure need to be regenerated to represent the current state of the place bin accurately. How do you address this? As far as I understood from the paper(correct me if I am wrong), only the leaf nodes are removed if they are not valid but what about the internal & leaf nodes whose values are slightly off?

2. Orientation representation in state: While considering a new incoming item(Sx,Sy,Sz), if we want to consider the placement with more than one orientation for the item, how are these orientation values encoded into the state representation.