* Added basic functionality and tests
* Feature parity with old tune search space config
* Convert Optuna search spaces
* Introduced quantized values
* Updated Optuna resolving
* Added HyperOpt search space conversion
* Convert search spaces to AxSearch
* Convert search spaces to BayesOpt
* Added basic functionality and tests
* Feature parity with old tune search space config
* Convert Optuna search spaces
* Introduced quantized values
* Updated Optuna resolving
* Added HyperOpt search space conversion
* Convert search spaces to AxSearch
* Convert search spaces to BayesOpt
* Re-factored samplers into domain classes
* Re-added base classes
* Re-factored into list comprehensions
* Added `from_config` classmethod for config conversion
* Applied suggestions from code review
* Removed truncated normal distribution
* Set search properties in tune.run
* Added test for tune.run search properties
* Move sampler initializers to base classes
* Add tune API sampling test, fixed includes, fixed resampling bug
* Add to API docs
* Fix docs
* Update metric and mode only when set. Set default metric and mode to experiment analysis object.
* Fix experiment analysis tests
* Raise error when delimiter is used in the config keys
* Added randint/qrandint to API docs, added additional check in tune.run
* Fix tests
* Fix linting error
* Applied suggestions from code review. Re-aded tune.function for the time being
* Fix sampling tests
* Fix experiment analysis tests
* Fix tests and linting error
* Removed unnecessary default_config attribute from OptunaSearch
* Revert to set AxSearch default metric
* fix-min-max
* fix
* nits
* Added function check, enhanced loguniform error message
* fix-print
* fix
* fix
* Raise if unresolved values are in config and search space is already set
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
* create guide gallery for Tune
* mods
* ok
* fix
* fix_up_gallery
* ok
* Apply suggestions from code review
Co-Authored-By: Sven Mika <sven@anyscale.io>
* Apply suggestions from code review
Co-Authored-By: Sven Mika <sven@anyscale.io>
Co-authored-by: Sven Mika <sven@anyscale.io>
* Add base for Soft Actor-Critic
* Pick changes from old SAC branch
* Update sac.py
* First implementation of sac model
* Remove unnecessary SAC imports
* Prune unnecessary noise and exploration code
* Implement SAC model and use that in SAC policy
* runs but doesn't learn
* clear state
* fix batch size
* Add missing alpha grads and vars
* -200 by 2k timesteps
* doc
* lazy squash
* one file
* ignore tfp
* revert done
