* Changed Shortest Path Follower criteria to use geodesic distance

* Fixed bug with uneven splits for dataset
* Fixed check if test data was loaded for CI

Added proper check of test data installed to fix CI.

This PR seeks to bring habitat-API inline with this. StaticPointGoalSensor is now just a PointGoalSensor and the old PointGoalSensor is now a PointGoalWithGPSCompassSensor. There are also now explicit GPS and Compass sensors, such that you can fairly easily compute PointGoalWithGPSCompassSensor given [PointGoalSensor, GPSSensor, CompassSensor].
The PointGoal* sensors now also include {2d, 3d} x {Cartesian, Polar} support.

Explicitly call close for vector env to avoid issues with file writer prematurely close buffer when tensorboard enabled. See #184 for more info.

Making eval() in ppo_trainer retrieve the name (sensor_uuid) of performance metric used instead of always using spl. This change is necessary for ppo_trainer to support new tasks that might have performance metric other than spl.

With the recent refactor/redesign of habitat_baselines, all experiment parameters can be specified through config files. Thus it makes sense to turn the python script run in circle-ci into a separate test file in our test/. This also makes it more extensible when we add more trainers in the future.

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* before doc

* use iter getter instead of as a dataset attr

* fix docstrings

* changes according to comment

* fix long var name

* use default value in next

* fix baseline test import and docstring

* lr scheduler and tb writer

* refactor config

* More Env typing fixes

* Move log_file config entry

* fix tb-related VectorEnv del bug

* change config entries to list

* fix env class retrieval

* change default opts to empty list

* changes according to comments

* convert jupyter notebooks to sphynx tutorial

* update download links

* habitat-sim demo, update image links

* add documentation link, remove PRs welcome

* nit

* remove habitat-api_structure.png

* Refactor baselines to allow for extension

* Make clip an after-backward

* Loop over rollout step instead of function for full rollout

* Make fog-of-war darker and make that value changeable

Update baseline test import and fix docstring 

* refactor and  add generic trainer class

* fix to pass tests

* change BaseModel to BaseTrainer

* fix tensorboard import causing CI failure

* modify circle-ci test script accordingly

* doc, typing and other changes

* rename BASELINE to TRAINER

* merge from upstream master

* Update Habitat-API to allow for no rendering sensors (#139)

Update Habitat-API to allow for no rendering sensors

* Added installation requirements step for sim installation in CI setup. (#159)

Added installation requirements step for sim installation in CI setup

* move RolloutStorage to utils

* add environments.py

* make ckpt dir if not exist

* small fixes according to comments

* changes according to comments

* update readme

* fix old config compatibility

* fix missed isort lint

* fixed PyTorch caching
* added ccache to CI
* broke habitat api step on installation and testing to save extra 5 min on saving cache in parallel
* testing time drops from first run to second run from 17.5 minutes to 9 minutes (2x times) for full testing.

* Add fog-of-war to topdown map

Make env access episode through iterator

Added installation requirements step for sim installation in CI setup

Update Habitat-API to allow for no rendering sensors

This does the necessary adaptation to facebookresearch/habitat-sim#64 -- this was the only failure in habitat-sim's CI tests

* episode stats changed to dict of dicts

* handle non-unique episode_id

* use tuple as key

* update while loop checking

Co-Authored-By: Erik Wijmans <ewijmans2@gmail.com>

* made simulator actions iterable

* Fix quickstart

* Add checkpoint progress tracking for evalute_ppo. Now when specified with a checkpoint directory,
* Evaluate_ppo will evaluate checkpoints in chronological order, and constantly check for new checkpoint.
* Add tensorboard visualization to both train_ppo and evaluate_ppo
* Add video generation for navigation episode evaluation. Generated videos can be either saved locally or visualized through tensorboard.
* Add shortest path visualization

Updated version to 0.1.2

Limit requirements search scope to habitat_baselines folder.