One After Another: Learning Incremental Skills for a Changing World
Nur Muhammad Shafiullah, Lerrel Pinto

TL;DR
This paper introduces an incremental skill learning framework that enables agents to adapt to changing environments without forgetting previous skills, outperforming existing methods in static and evolving settings.
Contribution
The authors propose a novel incremental skill discovery approach that sequentially learns skills, balancing adaptation to new environments with retention of prior skills.
Findings
Incremental skills outperform state-of-the-art methods in static environments.
The approach enables fast adaptation to environment changes.
Agents retain previously learned skills effectively.
Abstract
Reward-free, unsupervised discovery of skills is an attractive alternative to the bottleneck of hand-designing rewards in environments where task supervision is scarce or expensive. However, current skill pre-training methods, like many RL techniques, make a fundamental assumption - stationary environments during training. Traditional methods learn all their skills simultaneously, which makes it difficult for them to both quickly adapt to changes in the environment, and to not forget earlier skills after such adaptation. On the other hand, in an evolving or expanding environment, skill learning must be able to adapt fast to new environment situations while not forgetting previously learned skills. These two conditions make it difficult for classic skill discovery to do well in an evolving environment. In this work, we propose a new framework for skill discovery, where skills are learned…
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Taxonomy
TopicsReinforcement Learning in Robotics · Artificial Intelligence in Games · Multimodal Machine Learning Applications
