* 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
* 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
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
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.
* reordering of the episode reset operation and allowing to store episodes only when they are terminated
* reordering of the episode reset operation and allowing to store episodes only when they are terminated
* revert tensorflow-gpu to 1.9.0 + bug fix in should_train()
* tests readme file and refactoring of policy optimization agent train function
* Update README.md
* Update README.md
* additional policy optimization train function simplifications
* Updated the traces after the reordering of the environment reset
* docker and jenkins files
* updated the traces to the ones from within the docker container
* updated traces and added control suite to the docker
* updated jenkins file with the intel proxy + updated doom basic a3c test params
* updated line breaks in jenkins file
* added a missing line break in jenkins file
* refining trace tests ignored presets + adding a configurable beta entropy value
* switch the order of trace and golden tests in jenkins + fix golden tests processes not killed issue
* updated benchmarks for dueling ddqn breakout and pong
* allowing dynamic updates to the loss weights + bug fix in episode.update_returns
* remove docker and jenkins file
