Meta-learning within Projective Simulation

TL;DR

This paper extends the projective simulation model of AI to include meta-learning, enabling agents to autonomously adjust their learning parameters in reinforcement learning tasks, improving adaptability and performance without manual tuning.

Contribution

It introduces a meta-learning scheme within projective simulation, allowing autonomous adjustment of meta-parameters using clip networks, and demonstrates its effectiveness across various tasks.

Findings

Achieves near-optimal success rates without manual meta-parameter tuning

Demonstrates utility of reflexive and learning-based adaptation approaches

Addresses trade-off between flexibility and learning time

Abstract

Learning models of artificial intelligence can nowadays perform very well on a large variety of tasks. However, in practice different task environments are best handled by different learning models, rather than a single, universal, approach. Most non-trivial models thus require the adjustment of several to many learning parameters, which is often done on a case-by-case basis by an external party. Meta-learning refers to the ability of an agent to autonomously and dynamically adjust its own learning parameters, or meta-parameters. In this work we show how projective simulation, a recently developed model of artificial intelligence, can naturally be extended to account for meta-learning in reinforcement learning settings. The projective simulation approach is based on a random walk process over a network of clips. The suggested meta-learning scheme builds upon the same design and employs clip networks to monitor the agent's performance and to adjust its meta-parameters "on the fly". We distinguish between "reflexive adaptation" and "adaptation through learning", and show the utility of both approaches. In addition, a trade-off between flexibility and learning-time is addressed. The extended model is examined on three different kinds of reinforcement learning tasks, in which the agent has different optimal values of the meta-parameters, and is shown to perform well, reaching near-optimal to optimal success rates in all of them, without ever needing to manually adjust any meta-parameter.