1. Having the standard checkpoint prefix in order for the data store to grab it, and sync it to S3.
2. Removing the reference to Redis so that it won't try to pickle that in.
3. Enable restoring a checkpoint into a single-worker run, which was saved by a single-node-multiple-worker run.
* Adding checkpointing framework as well as mxnet checkpointing implementation.
- MXNet checkpoint for each network is saved in a separate file.
* Adding checkpoint restore for mxnet to graph-manager
* Add unit-test for get_checkpoint_state()
* Added match.group() to fix unit-test failing on CI
* Added ONNX export support for MXNet
* Enable setting the data store factory in Graph manager
This change enables us to use custom data store for storing and retrieving models.
We currently need this to have use a data store that loads temporary AWS credentials
from disk before calling store or load operations.
* Removed data store factory and introduced data store as a attribute
* Allow arbitrary dimensional observation (non vector or image)
* Added creating PlanarMapsObservationSpace to GymEnvironment when number of channels is not 1 or 3
* Added NormalizedRSSInitializer, using same method as TensorFlow backend, but changed name since ‘columns’ have different meaning in dense layer weight matrix in MXNet.
* Added unit test for NormalizedRSSInitializer.
* Changes required for Continuous PPO Head with MXNet. Used in MountainCarContinuous_ClippedPPO.
* Simplified changes for continuous ppo.
* Cleaned up to avoid duplicate code, and simplified covariance creation.
* updating the documentation website
* adding the built docs
* update of api docstrings across coach and tutorials 0-2
* added some missing api documentation
* New Sphinx based documentation
* create per environment Dockerfiles.
Adjust CI setup to better parallelize runs.
Fix a couple of issues in golden and trace tests.
Update a few of the docs.
* bugfix in mmc agent.
Also install kubectl for CI, update badge branch.
* remove integration test parallelism.
Adding mxnet components to rl_coach architectures.
- Supports PPO and DQN
- Tested with CartPole_PPO and CarPole_DQN
- Normalizing filters don't work right now (see #49) and are disabled in CartPole_PPO preset
- Checkpointing is disabled for MXNet
* refactoring the merging of the task parameters and the command line parameters
* removing some unused command line arguments
* fix for saving checkpoints when not passing through coach.py
