Trainer implementation

[theOGognf]

A trainer is a level above an algorithm in that the trainer is used for running training routines/jobs and has interfaces for tracking experiments, checkpointing, evaluating, etc.. The algorithm is solely focused on updating the policy and collecting environment samples while the trainer is focused on all the workflows associated with training.

The trainer should have the following methods:

• train for running one training step and logging metrics to a tensorboard logger
• eval for running one evaluation step and logging metrics to a tensorboard logger
• describe for describing the trainer's parameters and all its current metrics (average reward, min reward, max reward, most recent losses)
• checkpoint for checkpointing the trainer and all its underlying pieces
• run for running all the above methods with some config options

[theOGognf]

Most of the GPU algo implementation is done. we can start the trainer implementation once the algo testing is done

[theOGognf]

Moved to the rlstack project