Procedural generation of meta-reinforcement learning tasks

• URL: http://arxiv.org/abs/2302.05583v2
• Date: Sat, 9 Dec 2023 04:52:58 GMT
• Title: Procedural generation of meta-reinforcement learning tasks
• Authors: Thomas Miconi
• Abstract summary: We describe a parametrized space for simple meta-reinforcement learning (meta-RL) tasks with arbitrary stimuli. The parametrization is expressive enough to include many well-known meta-RL tasks, such as bandit problems, the Harlow task, T-mazes, the Daw two-step task and others. We describe a number of randomly generated meta-RL domains of varying complexity and discuss potential issues arising from random generation.
• Score: 1.2328446298523066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open-endedness stands to benefit from the ability to generate an infinite variety of diverse, challenging environments. One particularly interesting type of challenge is meta-learning ("learning-to-learn"), a hallmark of intelligent behavior. However, the number of meta-learning environments in the literature is limited. Here we describe a parametrized space for simple meta-reinforcement learning (meta-RL) tasks with arbitrary stimuli. The parametrization allows us to randomly generate an arbitrary number of novel simple meta-learning tasks. The parametrization is expressive enough to include many well-known meta-RL tasks, such as bandit problems, the Harlow task, T-mazes, the Daw two-step task and others. Simple extensions allow it to capture tasks based on two-dimensional topological spaces, such as full mazes or find-the-spot domains. We describe a number of randomly generated meta-RL domains of varying complexity and discuss potential issues arising from random generation.

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