* 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
* Integrate coach.py params with distributed Coach.
* Minor improvements
- Use enums instead of constants.
- Reduce code duplication.
- Ask experiment name with timeout.
NOTE: tensorflow framework works fine if mxnet is not installed in env, but mxnet will not work if tensorflow is not installed because of the code in network_wrapper.
