How to Learn and Represent Abstractions: An Investigation using Symbolic Alchemy

Badr AlKhamissi, Akshay Srinivasan, Zeb Kurth-Nelson, Samuel Ritter

Abstract: Alchemy is a new meta-learning environment rich enough to contain interesting abstractions, yet simple enough to make fine-grained analysis tractable. Further, Alchemy provides an optional symbolic interface that enables meta-RL research without a large compute budget. In this work, we take the first steps toward using Symbolic Alchemy to identify design choices that enable deep-RL agents to learn various types of abstraction. Then, using a variety of behavioral and introspective analyses we investigate how our trained agents use and represent abstract task variables, and find intriguing connections to the neuroscience of abstraction. We conclude by discussing the next steps for using meta-RL and Alchemy to better understand the representation of abstract variables in the brain.

Keywords: Symbolic Alchemy, Meta-RL, Abstraction, Neuroscience

One-sentence Summary: Using a variety of behavioral and introspective analyses we investigate how our trained agents use and represent abstract task variables in Symbolic Alchemy.

Track: Main track

Reproducibility Checklist: Yes

Broader Impact Statement: Yes

Paper Availability And License: Yes

Code Of Conduct: Yes

Reviewers: Badr AlKhamissi, [e-mail]

CPU Hours: 0

GPU Hours: 0

TPU Hours: 0

Evaluation Metrics: No

Class Of Approaches: Meta Reinforcement Learning

Datasets And Benchmarks: Symbolic Alchemy

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/how-to-learn-and-represent-abstractions-an/code)