* Fix Windows IL2CPP compilation. This does not address the issues with training when IL2CPP is enabled.
Co-authored-by: Jérémy Graziani <AcelisWeaven@users.noreply.github.com>
* tests WIP
* add test for misses, remove obsolete test
* add filtering test
* cleanup
* 2d default, remove constants test
* use built in tags
* copy old physics settings
* manually sync physics transforms
In the previous PR, steps were processed when the env manager was reset. This was an issue for the very first reset, where we don't actually know which agent groups (and AgentManagers) we needed to send the steps to. These steps were being thrown away.
This PR moves the processing of steps to advance(), so that the initial reset steps are simply processed when the next advance(). This also removes the need for an additional block of code in TrainerController to handle the initial reset.
Convert the UnitySDK to a Packman Package.
- Separate Examples into a sample project.
- Move core UnitySDK Code into com.unity.ml-agents.
- Create asmdefs for the ml-agents package.
- Add package validation tests for win/linux/max.
- Update protobuf generation scripts.
- Add Barracuda as a package dependency for ML-Agents. (users no longer have to install it themselves).
* Made the Agent reset immediately
* fixing the C# tests
* Fixing the tests still
* Trying with incremental episode ids
* deleting buffer rather than using an empty list
* Addressing the comments
* Forgot to edit the comment on AgentInfo
* Updating the migrating doc
* Fixed an obvious bug
* cleaning after an agent is done in agent processor
* Fixing the pytest errors
* Triming some of the methods of the agent but left SetReward
* Fixing bugs
* modifying the environments
* Reintroducing IsDone and IsMaxStepReached
* Updating the Migrating doc
* more details on the Migration
This PR moves the AgentManagers from the TrainerController into the env_manager. This way, the TrainerController only needs to create the components (Trainers, AgentManagers) and call advance() on the EnvManager and the Trainers.
* Simplifying the Agent reset logic
- Agents will reset in ResetIfDone immediately after being marked Done
- Agents will always request a decision right after reset
- This change implies that additional messages might be sent to Python
* Fixing the Unit Tests
* Added a note in the Migrating.md document
GitHub
Chris Elion