Learning with AMIGo: Adversarially Motivated Intrinsic Goals

International Conference on Learning Representations (ICLR)


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. We show that our method generates a natural curriculum of self-proposed goals which ultimately allows the agent to solve challenging procedurally-generated tasks where other forms of intrinsic motivation and state-of-the-art RL methods fail.

Related Publications

All Publications

SIGGRAPH - August 9, 2021

ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation

He Zhang, Yuting Ye, Takaaki Shiratori, Taku Komura

SIGGRAPH - August 9, 2021

Control Strategies for Physically Simulated Characters Performing Two-player Competitive Sports

Jungdam Won, Deepak Gopinath, Jessica Hodgins

CVPR - June 20, 2021

Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos

Yanghao Li, Tushar Nagarajan, Bo Xiong, Kristen Grauman

ICML - July 18, 2021

Align, then memorise: the dynamics of learning with feedback alignment

Maria Refinetti, Stéphane d'Ascoli, Ruben Ohana, Sebastian Goldt

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy