Fine tuning pre-trained agent

High Level Description Fine-tune the "RL-Agent" provided inside the model zoo, by specifying a new OpenAI Gym environment with customly-defined rewards. We tried several configurations, from understanding how to load the pre-trained weights inside Ray, to instantiate a model and replace the weights with the pre-trained ones, or by trying to registering the model inside Ray, but no strategies leaded us to anything working.

Desired SMARTS version 0.6.1rc1

Operating System Docker, Ubuntu 16.04

Problems The agent is correctly instantiated, but it is not clear how to fine-tune the created pretrained model. We tried to customize the rllib.py by instantiating the agent and passing it to Ray, through tune.run, but we were unsuccessful. Also, inside the rlagent script, there is a warning from developers that the zoo model is not trainable, but it is provided for evaluation only. No further explanations are given.