1594467000
Unity ml-agents 学習できない Couldn't connect
Unityとml-agentsを用いて機械学習したい
どのように環境構築を設定したら、Unityで学習ができるのでしょうか？
発生している問題・エラーメッセージ
mlagents-learn ./config/trainer_config.yaml --run-id=～～～と入力
2020-05-28 19:37:39.944015: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2020-05-28 19:37:39.948579: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
WARNING:tensorflow:From D:\Anaconda\envs\ml-agents\lib\site-packages\tensorflow\python\compat\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
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Version information:
ml-agents: 0.16.0,
ml-agents-envs: 0.16.0,
Communicator API: 1.0.0,
TensorFlow: 2.2.0
2020-05-28 19:37:41.755400: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2020-05-28 19:37:41.760354: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
WARNING:tensorflow:From D:\Anaconda\envs\ml-agents\lib\site-packages\tensorflow\python\compat\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
2020-05-28 19:37:43 INFO [environment.py:201] Listening on port 5004\. Start training by pressing the Play button in the Unity Editor.
anaconda promptではタイムアウトとして以下のメッセージが出る。
2020-05-28 19:38:43 INFO [subprocess_env_manager.py:191] UnityEnvironment worker 0: environment stopping.
Traceback (most recent call last):
File "D:\Anaconda\envs\ml-agents\Scripts\mlagents-learn-script.py", line 11, in <module>
load_entry_point('mlagents', 'console_scripts', 'mlagents-learn')()
File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\learn.py", line 554, in main
run_cli(parse_command_line())
File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\learn.py", line 550, in run_cli
run_training(run_seed, options)
File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\learn.py", line 407, in run_training
tc.start_learning(env_manager)
File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents-envs\mlagents_envs\timers.py", line 305, in wrapped
return func(*args, **kwargs)
File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\trainer_controller.py", line 223, in start_learning
self._reset_env(env_manager)
File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents-envs\mlagents_envs\timers.py", line 305, in wrapped
return func(*args, **kwargs)
File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\trainer_controller.py", line 154, in _reset_env
env.reset(config=sampled_reset_param)
File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\env_manager.py", line 67, in reset
self.first_step_infos = self._reset_env(config)
File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\subprocess_env_manager.py", line 295, in _reset_env
ew.previous_step = EnvironmentStep(ew.recv().payload, ew.worker_id, {}, {})
File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\subprocess_env_manager.py", line 92, in recv
raise env_exception
mlagents_envs.exception.UnityTimeOutException: The Unity environment took too long to respond. Make sure that :
The environment does not need user interaction to launch
The Agents are linked to the appropriate Brains
The environment and the Python interface have compatible versions.
What is GEEK
Buddha Community
1597222800
PostgreSQL Connection Pooling: Part 4 – PgBouncer vs. Pgpool-II
In our previous posts in this series, we spoke at length about using PgBouncer and Pgpool-II , the connection pool architecture and pros and cons of leveraging one for your PostgreSQL deployment. In our final post, we will put them head-to-head in a detailed feature comparison and compare the results of PgBouncer vs. Pgpool-II performance for your PostgreSQL hosting !
The bottom line – Pgpool-II is a great tool if you need load-balancing and high availability. Connection pooling is almost a bonus you get alongside. PgBouncer does only one thing, but does it really well. If the objective is to limit the number of connections and reduce resource consumption, PgBouncer wins hands down.
It is also perfectly fine to use both PgBouncer and Pgpool-II in a chain – you can have a PgBouncer to provide connection pooling, which talks to a Pgpool-II instance that provides high availability and load balancing. This gives you the best of both worlds!
PostgreSQL Connection Pooling: Part 4 – PgBouncer vs. Pgpool-II
Performance Testing
While PgBouncer may seem to be the better option in theory, theory can often be misleading. So, we pitted the two connection poolers head-to-head, using the standard pgbench tool, to see which one provides better transactions per second throughput through a benchmark test. For good measure, we ran the same tests without a connection pooler too.
Testing Conditions
All of the PostgreSQL benchmark tests were run under the following conditions:
- Initialized pgbench using a scale factor of 100.
- Disabled auto-vacuuming on the PostgreSQL instance to prevent interference.
- No other workload was working at the time.
- Used the default pgbench script to run the tests.
- Used default settings for both PgBouncer and Pgpool-II, except max_children*. All PostgreSQL limits were also set to their defaults.
- All tests ran as a single thread, on a single-CPU, 2-core machine, for a duration of 5 minutes.
- Forced pgbench to create a new connection for each transaction using the -C option. This emulates modern web application workloads and is the whole reason to use a pooler!
We ran each iteration for 5 minutes to ensure any noise averaged out. Here is how the middleware was installed:
- For PgBouncer, we installed it on the same box as the PostgreSQL server(s). This is the configuration we use in our managed PostgreSQL clusters. Since PgBouncer is a very light-weight process, installing it on the box has no impact on overall performance.
- For Pgpool-II, we tested both when the Pgpool-II instance was installed on the same machine as PostgreSQL (on box column), and when it was installed on a different machine (off box column). As expected, the performance is much better when Pgpool-II is off the box as it doesn’t have to compete with the PostgreSQL server for resources.
Throughput Benchmark
Here are the transactions per second (TPS) results for each scenario across a range of number of clients:
#database #developer #performance #postgresql #connection control #connection pooler #connection pooler performance #connection queue #high availability #load balancing #number of connections #performance testing #pgbench #pgbouncer #pgbouncer and pgpool-ii #pgbouncer vs pgpool #pgpool-ii #pooling modes #postgresql connection pooling #postgresql limits #resource consumption #throughput benchmark #transactions per second #without pooling
1607328200
Why unity 3D is best for game app development
We can see an exponential growth in the game development industry today and the market for game development will increase day by day ,thanks to the increasing number of smartphone users and the technological advancements.Unity 3D is the trending game app development framework to serve the best quality.This game development framework enables developers to conduct 2D or 3D rendering with more than 1 mobile game to assist them in ratcheting. Apart from this the great qualities like cross-platform integration with asset management, high-end visual quality, intuitive design, interface flexibility and gameplay can now be leveraged.India is the leading game development hub and now people are** hire dedicated unity 3D developers in India** to create a high performing game app with best quality at affordable price which you can spread your games to larger audience.Lets have a look at why unity a 3D is the best platform for game development.
**
Support cross-platform**
Cross platforms save time and money as a single script can be compiled and used for multiple platforms such as Android, iOS, PC, Web and even Mac etcFeatures such as agile methodology allow speedy prototyping and constant releases to speed up the process of game development.
Open source
The large open source community of Unity 3D with an easy-to-understand documentation allows developers to come up with the most accurate and precise code and it saves a lot of time.
Graphics
Unity 3D can support graphic rendering from engines that use OpenGL ES, OpenGL and Direct 3D, as well as applications like 3DS Max, Blender and Adobe Photoshop. It enables high-quality audio and visual effects to be adapted without any distortion or compromise with quality.
**
Play mode feature
**
This feature allows easy and hassle free testing by allowing developers to look and play within the game instantly, evaluate and even review it,and also the Play or Play Plus mode can also be used to achieve frame to frame referencing.
Debugging
With Unity game development, the analysis and modification is incredibly easier as all the game factors are seen during ongoing interaction, which helps the engineers to troubleshoot the process at runtime.
These advantages make unity as the best game development platform and people h**ire dedicated unity 3D developers** for the best output.With Unity, countless games have been made and some of them have become instant classics.Take a look at some of the all-time trending Unity games .
-
Kerbal Space Program
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Firewatch
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Subnautica
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Hollow Knight
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Arizona Sunshine
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Cuphead
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Ori And The Blind Forest
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Hearthstone
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Beat Saber
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Cities Skylines
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Getting Over It With Bennett Foddy
In terms of graphics, gameplay, consistency and realism, technical advances and rise of new technologies like AR & VR and AI & ML make the game more ambitious day by day.Today the entire global game development is booming and mobile gaming business are hire unity 3D developers in India to meet this heavy market.**Hire dedicated unity 3D developers **will benefits the following, -
International standard game app development at lower cost.
-
Skilled and experienced game developers
-
Faster time to market
-
Best infrastructure
Conclusion
Unity 3D has taken over the business and has altered the advancement of cross-platform app development paths. Unity 3D has already become the favourite of developers as they can import games created from iOS, PC, Play Store or other game consoles from other platforms and allow minimum game modifications to take full advantage of Unity 3D’s features. So if you have any game development hire unity 3D developers with great experience.
#hire unity 3 d developers in india #hire dedicated unity 3 d developers in india #hire unity 3 d programmers in india #hire unity 3 d developers #hire dedicated unity 3 d developers #hire unity 3 d programmers
1686080940
ML-Agents: Unity ML-Agents Toolkit
Unity ML-Agents Toolkit
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. We provide implementations (based on PyTorch) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. Researchers can also use the provided simple-to-use Python API to train Agents using reinforcement learning, imitation learning, neuroevolution, or any other methods. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. The ML-Agents Toolkit is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity’s rich environments and then made accessible to the wider research and game developer communities.
Features
- 17+ example Unity environments
- Support for multiple environment configurations and training scenarios
- Flexible Unity SDK that can be integrated into your game or custom Unity scene
- Support for training single-agent, multi-agent cooperative, and multi-agent competitive scenarios via several Deep Reinforcement Learning algorithms (PPO, SAC, MA-POCA, self-play).
- Support for learning from demonstrations through two Imitation Learning algorithms (BC and GAIL).
- Quickly and easily add your own custom training algorithm and/or components.
- Easily definable Curriculum Learning scenarios for complex tasks
- Train robust agents using environment randomization
- Flexible agent control with On Demand Decision Making
- Train using multiple concurrent Unity environment instances
- Utilizes the Unity Inference Engine to provide native cross-platform support
- Unity environment control from Python
- Wrap Unity learning environments as a gym environment
- Wrap Unity learning environments as a PettingZoo environment
See our ML-Agents Overview page for detailed descriptions of all these features. Or go straight to our web docs.
Releases & Documentation
Our latest, stable release is Release 20. Click here to get started with the latest release of ML-Agents.
You can also check out our new web docs!
The table below lists all our releases, including our main branch which is under active development and may be unstable. A few helpful guidelines:
- The Versioning page overviews how we manage our GitHub releases and the versioning process for each of the ML-Agents components.
- The Releases page contains details of the changes between releases.
- The Migration page contains details on how to upgrade from earlier releases of the ML-Agents Toolkit.
- The Documentation links in the table below include installation and usage instructions specific to each release. Remember to always use the documentation that corresponds to the release version you're using.
- The
com.unity.ml-agentspackage is verified for Unity 2020.1 and later. Verified packages releases are numbered 1.0.x.
| Version | Release Date | Source | Documentation | Download | Python Package | Unity Package |
|---|---|---|---|---|---|---|
| Release 20 | November 21, 2022 | source | docs | download | 0.30.0 | 2.3.0 |
| main (unstable) | -- | source | docs | download | -- | -- |
| Verified Package 1.0.8 | May 26, 2021 | source | docs | download | 0.16.1 | 1.0.8 |
If you are a researcher interested in a discussion of Unity as an AI platform, see a pre-print of our reference paper on Unity and the ML-Agents Toolkit.
If you use Unity or the ML-Agents Toolkit to conduct research, we ask that you cite the following paper as a reference:
@article{juliani2020,
title={Unity: A general platform for intelligent agents},
author={Juliani, Arthur and Berges, Vincent-Pierre and Teng, Ervin and Cohen, Andrew and Harper, Jonathan and Elion, Chris and Goy, Chris and Gao, Yuan and Henry, Hunter and Mattar, Marwan and Lange, Danny},
journal={arXiv preprint arXiv:1809.02627},
year={2020}
}
Additionally, if you use the MA-POCA trainer in your research, we ask that you cite the following paper as a reference:
@article{cohen2022,
title={On the Use and Misuse of Abosrbing States in Multi-agent Reinforcement Learning},
author={Cohen, Andrew and Teng, Ervin and Berges, Vincent-Pierre and Dong, Ruo-Ping and Henry, Hunter and Mattar, Marwan and Zook, Alexander and Ganguly, Sujoy},
journal={RL in Games Workshop AAAI 2022},
year={2022}
}
Additional Resources
We have a Unity Learn course, ML-Agents: Hummingbirds, that provides a gentle introduction to Unity and the ML-Agents Toolkit.
We've also partnered with CodeMonkeyUnity to create a series of tutorial videos on how to implement and use the ML-Agents Toolkit.
We have also published a series of blog posts that are relevant for ML-Agents:
- (July 12, 2021) ML-Agents plays Dodgeball
- (May 5, 2021) ML-Agents v2.0 release: Now supports training complex cooperative behaviors
- (December 28, 2020) Happy holidays from the Unity ML-Agents team!
- (November 20, 2020) How Eidos-Montréal created Grid Sensors to improve observations for training agents
- (November 11, 2020) 2020 AI@Unity interns shoutout
- (May 12, 2020) Announcing ML-Agents Unity Package v1.0!
- (February 28, 2020) Training intelligent adversaries using self-play with ML-Agents
- (November 11, 2019) Training your agents 7 times faster with ML-Agents
- (October 21, 2019) The AI@Unity interns help shape the world
- (April 15, 2019) Unity ML-Agents Toolkit v0.8: Faster training on real games
- (March 1, 2019) Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference
- (December 17, 2018) ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning
- (October 2, 2018) Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit
- (September 11, 2018) ML-Agents Toolkit v0.5, new resources for AI researchers available now
- (June 26, 2018) Solving sparse-reward tasks with Curiosity
- (June 19, 2018) Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree
- (May 24, 2018) Imitation Learning in Unity: The Workflow
- (March 15, 2018) ML-Agents Toolkit v0.3 Beta released: Imitation Learning, feedback-driven features, and more
- (December 11, 2017) Using Machine Learning Agents in a real game: a beginner’s guide
- (December 8, 2017) Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more
- (September 19, 2017) Introducing: Unity Machine Learning Agents Toolkit
- Overviewing reinforcement learning concepts (multi-armed bandit and Q-learning)
More from Unity
Community and Feedback
The ML-Agents Toolkit is an open-source project and we encourage and welcome contributions. If you wish to contribute, be sure to review our contribution guidelines and code of conduct.
For problems with the installation and setup of the ML-Agents Toolkit, or discussions about how to best setup or train your agents, please create a new thread on the Unity ML-Agents forum and make sure to include as much detail as possible. If you run into any other problems using the ML-Agents Toolkit or have a specific feature request, please submit a GitHub issue.
Please tell us which samples you would like to see shipped with the ML-Agents Unity package by replying to this forum thread.
Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few minutes to let us know about it.
For any other questions or feedback, connect directly with the ML-Agents team at ml-agents@unity3d.com.
Privacy
In order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics. Please refer to "Information that is passively collected by Unity" in the Unity Privacy Policy.
(latest release) (all releases)
Download Details:
Author: Unity-Technologies
Source Code: https://github.com/Unity-Technologies/ml-agents
License: View license
1613126944
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1612786985
Unity Game Development Company | Hire Unity Developers
Hire unity app developers of The NineHertz at nominal cost. We provide all the possible support and ease to make the client satisfied. From our in depth analysis, we are always able to deliver quality work at time. Not only the quality but we make sure the outcome or the gaming solution as per the guidelines of the client and has functionality which meets the needs of the market. The game software built by our experts is seamless and robust which increases the user popularity graph.
#unity game development #unity mobile app development #unity development services #unity game development company