* Builds and installs habitat-sim with python setup.py install
* Added pytorch installation and running habitat baseline test in prediction mode. That maybe too much of testing, but decided to keep it for now.
* Have ideas how to speed up and parallelize jobs. Will follow up in next PRs.
* Run isort and black on whole code to make it happy.
* Includes parallelization of installation steps and use files as flags that step is finished. CircleCI has no good way to parallelize steps and have dependencies one the same machine.
* Cache is used to save conda dependencies installed and build simulator based on commit SHA.

use attrs for defining things like Episodes and Goals as inheritance works beautifully and classes get a lot of helpful methods for free.

  * Add `test_static_pointgoal_senosr()` in `test/test_sensor.py`
  * Fix overriden configs in `test_sensors.py` (now using default config)
  * Change default config to include static pointgoal sensor
  * Fix `StaticPointGoalSensor._get_sensor_type()` to return `PATH`

* Added episode generation code for PointGoal task.
* Removed usage of internal methods, added island_radius method to HabitatSim
* Added generator test with shortest path proper test and episode serialization testing.
* Fixed formatting issues and unused imports across the codebase.
* Added editor config style formatter properties.

* added conversion of matrix map to color picture
* added example

* static PointGoal sensor, Top Down map measure
* top down map measure

* Made simple agents suitable for challenge submissions.

* Addressed the comments, chaned flag names, switched to benchmark

* Removed .swp file, added swp to gitignore.

* Compatability with habitat-sim#6

* Fix formatting

* Just use the quats to rotate thing

* Burn in looks good

* Fixup newly merged code wrt. this PR

* Nits

* Fixup quaternion usage

* Add TODO

Added collision and proximity measurements; Use is_navigable for a more robust check for top-down map

Benchmark and challenge implementations; PPO agents, Random, ForwardOnly, GoalFollower agents; High resolution top down map and bird-eye visualizations; Collisions measure introduced; SPL success criteria fix; Added deleter that closes subprocesses