This commit adds support for running Unity environments in parallel.
An abstract base class was created for UnityEnvironment which a new
SubprocessUnityEnvironment inherits from.
SubprocessUnityEnvironment communicates through a pipe in order to
send commands which will be run in parallel to its workers.
A few significant changes needed to be made as a side-effect:
* UnityEnvironments are created via a factory method (a closure)
rather than being directly created by the main process.
* In mlagents-learn "worker-id" has been replaced by "base-port"
and "num-envs", and worker_ids are automatically assigned across runs.
* BrainInfo objects now convert all fields to numpy arrays or lists to
avoid serialization issues.
* Added RenderTexture support for visual observations
* Cleaned up new ObservationToTexture function
* Added check for to width/height of RenderTexture
* Added check to hide HelpBox unless both cameras and RenderTextures are used
* Added documentation for Visual Observations using RenderTextures
* Added GridWorldRenderTexture Example scene
* Adjusted image size of doc images
* Added GridWorld example reference
* Fixed missing reference in the GridWorldRenderTexture scene and resaved the agent prefab
* Fix prefab instantiation and render timing in GridWorldRenderTexture
* Added screenshot and reworded documentation
* Unchecked control box
* Rename renderTexture
* Make RenderTexture scene default for GridWorld
Co-authored-by: Mads Johansen <pyjamads@gmail.com>
* fixed the test break on pytest > 4.0, added the pytest cov
* added the pytest-cov package
* added the logic to upload coverage.yml report to codacy
* remove the warning message in during the pytest
* added the codacy badge to show what it looks like
* added a space
* removed the space
* removed the duplicate pytest
* removed the extra spaces
* added the test coverage badge
* point the badge to the test branch
* changed
* moved the python test coverage to circleci
* removed the badge
* added the badge
* fixed the link
* Added the gym_unity test to the circleci
* Fixed the gym_unity installation
* Changed the test-reports from the ml-agents subfolder to the root folder, so that it covers gym_unity’s pytest also
* Garbage collection optimisations:
- Changed a few IEnumerable instances to IReadOnlyList. This avoids some unnecessary GC allocs that cast the Lists to IEnumerables.
- Moved cdf allocation outside of the loop to avoid unnecessary GC allocation.
- Changed GeneratorImpl to use plain float and int arrays instead of Array during generation. This avoids SetValue performing boxing on the arrays, which eliminates an awful lot of GC allocs.
* Convert InferenceBrain to use IReadOnlyList to avoid garbage creation.
* Fix typos
* Use abstract class for rayperception
* Created RayPerception2D. (#1721)
* Incorporate RayPerception2D
* Fix typo
* Make abstract class
* Add tests
* Fix for Brains not reinitialising when the scene is reloaded.
This was a bug caused by the conversion of Brains over to ScriptableObjects. ScriptableObjects persist in memory between scene changes, which means that after a scene change the Brains would still be initialised and the agentInfos list would contain invalid references to the Agents from the previous scene.
The fix is to have the Academy notify the Brains when it is destroyed. This allows the Brains to clean themselves up and transition back to an uninitialised state. After the new scene is loaded, the Brain's LazyInitialise will reconnect the Brain to the new Academy as expected.
* Fix for Brains not reinitialising when the scene is reloaded.
This was a bug caused by the conversion of Brains over to ScriptableObjects. ScriptableObjects persist in memory between scene changes, which means that after a scene change the Brains would still be...
* Move 'take_action' into Policy class
This refactor is part of Actor-Trainer separation. Since policies
will be distributed across actors in separate processes which share
a single trainer, taking an action should be the responsibility of
the policy.
This change makes a few smaller changes:
* Combines `take_action` logic between trainers, making it more
generic
* Adds an `ActionInfo` data class to be more explicit about the
data returned by the policy, only used by TrainerController and
policy for now.
* Moves trainer stats logic out of `take_action` and into
`add_experiences`
* Renames 'take_action' to 'get_action'
Removing this function breaks some tests, and the only way around
this at this time is a bigger refactor or hacky fixes to tests.
For now, I'd suggest we just revert this small part of a change
and keep a refactor in mind for the future.
* Ticked API :
- Ticked API for pypi for mlagents
- Ticked API for pypi for unity-gym
- Ticked Communication number for API
- Ticked Model Loader number for API
* Ticked the API for the pytest
* added the pypiwin32 package
* fixed the break on mac, fixed part of pytest above version 4
* added something to the windows to help unstuck people
* resolved the comment
* Switched default Mac GFX API to Metal
* Added Barracuda pre-0.1.5
* Added basic integration with Barracuda Inference Engine
* Use predefined outputs the same way as for TF engine
* Fixed discrete action + LSTM support
* Switch Unity Mac Editor to Metal GFX API
* Fixed null model handling
* All examples converted to support Barracuda
* Added model conversion from Tensorflow to Barracuda
copied the barracuda.py file to ml-agents/mlagents/trainers
copied the tensorflow_to_barracuda.py file to ml-agents/mlagents/trainers
modified the tensorflow_to_barracuda.py file so it could be called from mlagents
modified ml-agents/mlagents/trainers/policy.py to convert the tf models to barracuda compatible .bytes file
* Added missing iOS BLAS plugin
* Added forgotten prefab changes
* Removed GLCore GFX backend for Mac, because it doesn't support Compute shaders
* Exposed GPU support for LearningBrain inference
...
GitHub
eshvk
Arthur Juliani
Ervin T
Vincent-Pierre BERGES
vincentpierre
Jonathan Harper
Vincent(Yuan) Gao