* SAC algorithm
* SAC - updates to agent (learn_from_batch), sac_head and sac_q_head to fix problem in gradient calculation. Now SAC agents is able to train.
gym_environment - fixing an error in access to gym.spaces
* Soft Actor Critic - code cleanup
* code cleanup
* V-head initialization fix
* SAC benchmarks
* SAC Documentation
* typo fix
* documentation fixes
* documentation and version update
* README typo
* 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.
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
