End-to-End Deep Reinforcement Learning Without Reward Engineering

Soft Actor Critic (SAC) is a great method on it's own but this human-in-the-loop training for real world problems is a totally viable path for long term training of robots on challenging tasks.

The major roadblock we still face though is having a single model reach competency on multiple tasks without losing competency on earlier ones. I'm willing to train a robot once for each new task, but I'm not willing to constantly retrain it on old ones or wait for it to start from scratch after it's learned a few basic tasks.

We expect skill learning to be compositional and, like learning to ride a bike, easily retained.

This problem (often referred to as life-long learning, or overcoming catastrophic forgetting) is a deep problem in modern ML.