Campero, A;
Raileanu, R;
Küttler, H;
Tenenbaum, JB;
Rocktäschel, T;
Grefenstette, E;
(2021)
Learning with AMIGo: Adversarially Motivated Intrinsic Goals.
In:
(Proceedings) ICLR 2021: Ninth International Conference on Learning Representations.
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Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning with AMIGo: Adversarially Motivated Intrinsic Goals |
| Event: | ICLR 2021: Ninth International Conference on Learning Representations |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://openreview.net/group?id=ICLR.cc/2021/Confe... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10130728 |
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