1.0: DQNs and DDPGs

The library as it is allows for easy training for DDPG based models and DQN based models. You can also save them, and reload them.

You can use the different interpreter objects for graphing rewards, comparing rewards with other models, viewing episodes at different periods of the agent's training, etc.

Notes:
Currently, the next obstacle is memory efficiency. We will be adding more models, but will also be addressing memory issues possibly by off loading to storage.

Pre-1.0 Release

All gifs are added. There may be a few extra added in later versions, however all of the base env runs are there.

Don't get excited yet

Still does not contain Gifs. This is primarily a test of the azure pipeline publishing packages for us. Once a PR is pushed to master, the new version will automatically be updated in PyPI.

Next Release will have Gifs, then soon after redone readme

Stable (er) Contains 5-run tests of all models

Some key take aways with this release:

• 75% of the code has been trashed and replaced. Code is now organized more similarly with the fastai gan.py file as well as the computer vision models.
  • Uses fasti tabular model for embeddable and linear state / general inputs
• jupyter notebooks with executable code displaying graphs.
• More interpreter improvements

Why the changes?

• Main motivation is making the "trained" models as separate from the rest of the fastrl API. This means that the models are not longer having data / learner / Action / State objects embedded inside them. This should be portability easier. The main code base that is heavily integrated with fastrl will be part of the training, but you could / will be able to run the models without fastrl once trained (in theory, we'll see)

Stable (ish) Contains passing tests for all models

Right now some basic model configurations are complete with unit tests. Moving forward, we will be checking the model performance that is expected on a set of environments.

DDPG and DQN Unstable

Merge pull request #6 from josiahls/version_0_7_0

DDPG / Testing Init

DDPG / DQN Highly Unstable

Added:

 * Memory RAM size reduction via cleaning on item input.

Fixed:

 * DDPG is stable now. Works on Pendulum as expected / desired

Notes:

 * Now that DDPG works as expected, we will move to preparing repo for
 version 1.0. This will involve testing / CI and passing expected benchmarks.