* 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.

Fix issue with POINTNAVV1 fix.

SimulatorActions is no longer an enum. It is an extendable singleton. It allows you to extend the action space but it is quite strict about it. Actions cannot be put on different ints, actions cannot be remove.
There is now a registry for action space configurations.

Fix docs typo: step() -> act()

* Modify partitioning logic to handle left-over scenes due to integer division

Added description how to use config system, fixed missing copyright headers, improved example and isort CI (#125)

* Added description how to use config system related to #20.
* Added missing copyright headers
* Improve registry example to be cleaner
* Adde habitat_sim as first party lib to isort CI setup

* Generalize PPO with both PointNav goal sensors
* generalize simple agents and fix lint
* Removed dataset specific nodes

* Made isort for CI check only

* Added badges

Updated baselines readme, fixed links, added link to checkpoints.

* Fix the citation

* Caps

Expose other measures through get_info in NavRLEnv

* Adding jupyter notebook illustrating demonstrating how to extract camera parameters in the scene and how these camera parameters relate to the given views with proper testing.

* check scene paths, minor fix in pointnav generator

Fixes and upgrades the collision logic.

Asterisk for co-first author messes up alphabetical ordering in references section.

The habitat-api-demo.ipynb notebook illustrates how to specify config parameters, print semantic annotations of a scene. It also visualises the RGB, semantic and depth observations obtained by executing a random sequence of actions.

Adds documentation for habitat-api, to view the docs follow instructions in docs/README.md and view the html files from the build folder.

- add config option NUM_SAMPLE_EPISODE, with default value -1 that
indicates use all episodes. When set value to non-negative integer,
pointna_dataset will sample the specified number of episodes.
- fix the functionality of merging multiple config files. Now can specify
more than one config file like this
	--task-config "a.yaml" "b.yaml"

Our registry pattern is far from idea and thus we haven't used it consistent. This PR aims to resolve this by copying the registry pattern used in Pythia (as it is much nicer) and then registering all current measures and sensors and using the register to query for those instead of getattr as this is a more clearly extendable pattern (as compared to monkey patching).

Utility method for getting the current observation. Sometimes you want the observation without having to make a step/reset call for whatever reason.

* 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.

* Fix the broken tests by attr pr

* Update main README to properly install gym

* Update baseline README for gym install

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

Changing action space names to say what they actually do, adding the look up and look down actions.

* add extras_require in setup.py

* add exclude in setup.py for baselines

* add pytest as requirement

* Implement accessing setup() params within custom commands

  * 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`