* Check that worker port is available in RpcCommunicator
Previously the RpcCommunicator did not check the port or create the
RPC server until `initialize()` was called. Since "initialize"
requires the environment to be available, this means we might create
a new environment which connects to an existing RPC server running
in another process. This causes both training runs to fail.
As a remedy to this issue, this commit moves the server creation into
the RpcCommunicator constructor and adds an explicit socket binding
check to the requested port.
* Fixes suggested by Codacy
* Update rpc_communicator.py
* Addressing feedback: formatting & consistency
* Documentation Update
* addressed comments
* new images for the recorder
* Improvements to the docs
* Address the comments
* Core_ML typo
* Updated the links to inference repo
* Put back Inference-Engine.md
* fix typos : brain
* Readd deleted file
* fix typos
* Addressed comments
* Adding model for 3D Balance Ball.
* Adding LearningBrain to BroadCast Hub.
* Removed CrawlerPlayer Brain
* Renamed CrawlerLearning —> CrawlerStaticLearning
* Update Hallway models
* Attaching model to brain for Hallway
* Attaching model to 3DBall Brain.
* Updated CrawlerLearning —> CrawlerStaticLearning on trainer config.
* Adding Reacher model
* Remove model specification in Hallway Brain asset
* Removing model specification from 3Dball scene
* Adding crawler model file
* Specifying learning brain as default for crawler
The check for wether an agent has fallen off the platform was using a wrong value of 1 instead of 0.
This meant that the agent immediately started in a falling state and entered a thrashing cycle of resetting itself.
* Fix Typo #1323
* First update to the docs
* Addressed comments
* remove references to TF#
* Replaced the references to TF# with new document.
* Edditied the FAQ
* added faq to the aws doc
* added the link
* added some faq and updated the temp ami id
* resolved the comments, updated one of the faq along with the scriptable object update
* added one other cause raise in issues
* fixed line change
* Initial Commit
* attempt at refactor
* Put all static methods into the CoreInternalBrain
* improvements
* more testing
* modifications
* renamed epsilon
* misc
* Now supports discrete actions
* added discrete support and RNN and visual. Left to do is refactor and save variables into models
* code cleaning
* made a tensor generator and applier
* fix on the models.py file
* Moved the Checks to a different Class
* Added some unit tests
* BugFix
* Need to generate the output tensors as well as inputs before executing the graph
* Made NodeNames static and created a new namespace
* Added comments to the TensorAppliers
* Started adding comments on the TensorGenerators code
* Added comments for the Tensor Generator
* Moving the helper classes into a separate folder
* Added initial comments to the TensorChecks
* Renamed NodeNames -> TensorNames
* Removing warnings in tests
* Now using Aut...
GitHub
Jonathan Harper
Vincent(Yuan) Gao
eshvk
vincentpierre
Arthur Juliani