Learning with AMIGo: Adversarially Motivated Intrinsic Goals

Understand

A key challenge for reinforcement learning (RL) consists of learning in environments with sparse extrinsic rewards.

• In contrast to current RL methods, humans are able to learn new skills with little or no reward by using various forms of intrinsic motivation.
• We propose AMIGo, a novel agent incorporating -- as form of meta-learning -- a goal-generating teacher that proposes Adversarially Motivated Intrinsic Goals to train a goal-conditioned "student" policy in the absence of (or alongside) environment reward.
• Specifically, through a simple but effective "constructively adversarial" objective, the teacher learns to propose increasingly challenging -- yet achievable -- goals that allow the student to learn general skills for acting in a new environment, independent of the task to be solved.