Generating Automatic Curricula via Self-Supervised Active Domain Randomization

Understand

Goal-directed Reinforcement Learning (RL) traditionally considers an agent interacting with an environment, prescribing a real-valued reward to an agent proportional to the completion of some goal.

• Goal-directed RL has seen large gains in sample efficiency, due to the ease of reusing or generating new experience by proposing goals.
• One approach,self-play, allows an agent to "play" against itself by alternatively setting and accomplishing goals, creating a learned curriculum through which an agent can learn to accomplish progressively more difficult goals.
• However, self-play has been limited to goal curriculum learning or learning progressively harder goals within a single environment.