Curriculum Learning with a Progression Function
- URL: http://arxiv.org/abs/2008.00511v2
- Date: Sun, 31 Oct 2021 14:24:04 GMT
- Title: Curriculum Learning with a Progression Function
- Authors: Andrea Bassich, Francesco Foglino, Matteo Leonetti and Daniel Kudenko
- Abstract summary: This paper introduces a novel paradigm for curriculum generation based on progression and mapping functions.
Our approach's benefits and wide applicability are shown by empirically comparing its performance to two state-of-the-art Curriculum Learning algorithms.
- Score: 6.212955085775758
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Curriculum Learning for Reinforcement Learning is an increasingly popular
technique that involves training an agent on a sequence of intermediate tasks,
called a Curriculum, to increase the agent's performance and learning speed.
This paper introduces a novel paradigm for curriculum generation based on
progression and mapping functions. While progression functions specify the
complexity of the environment at any given time, mapping functions generate
environments of a specific complexity. Different progression functions are
introduced, including an autonomous online task progression based on the
agent's performance. Our approach's benefits and wide applicability are shown
by empirically comparing its performance to two state-of-the-art Curriculum
Learning algorithms on six domains.
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