removed the Unity connect link as some folks were submitting issues there and getting lost. we do not actively monitor the connect channel and do not want to cause confusion
Some of our tests fail with the new pytest v4.0.x versions because of
a change to how fixtures work. Since these versions are the latest
release in PyPI now, unit tests will fail on a fresh install.
This change limits the version range in setup.py to fix the issue
until we can make a decision about upgrading.
* Simplified rewards and observations; Determined better settings for training within a reasonable amount of time.
* Simplified Agent rewards; Added training section that discusses hyperparameters.
* Added note about DecisionFrequency.
* Updated screenshots and a small clarification in the text.
* Tested and updated using v0.6.
* Update a couple of images, minor text edit.
* Replace with more recent training stats.
* resolve a couple of minor review commnts.
* Increased the recommended batch and buffer size hyperparameter values.
* Fix 2 typos.
* 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.
vincentpierre
GitHub
Jonathan Harper
eshvk
Arthur Juliani
Vincent(Yuan) Gao