* 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
* 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
* 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
* Multiple improvements and bug fixes:
* Using lazy stacking to save on memory when using a replay buffer
* Remove step counting for evaluation episodes
* Reset game between heatup and training
* Major bug fixes in NEC (is reproducing the paper results for pong now)
* Image input rescaling to 0-1 is now optional
* Change the terminal title to be the experiment name
* Observation cropping for atari is now optional
* Added random number of noop actions for gym to match the dqn paper
* Fixed a bug where the evaluation episodes won't start with the max possible ale lives
* Added a script for plotting the results of an experiment over all the atari games
Main changes are detailed below:
New features -
* CARLA 0.7 simulator integration
* Human control of the game play
* Recording of human game play and storing / loading the replay buffer
* Behavioral cloning agent and presets
* Golden tests for several presets
* Selecting between deep / shallow image embedders
* Rendering through pygame (with some boost in performance)
API changes -
* Improved environment wrapper API
* Added an evaluate flag to allow convenient evaluation of existing checkpoints
* Improve frameskip definition in Gym
Bug fixes -
* Fixed loading of checkpoints for agents with more than one network
* Fixed the N Step Q learning agent python3 compatibility
* bug fix - QR-DQN using error instead of abs-error in the quantile huber loss
* improvement - QR-DQN sorting the quantile only once instead of batch_size times
* new feature - adding the Breakout QRDQN preset (verified to achieve good results)
Itai Caspi