Toward Complex and Structured Goals in Reinforcement Learning
Davidson, G., & Gureckis, T. M. (2024). Toward Complex and Structured Goals in Reinforcement Learning. Reinforcement Learning Conference (RLC) Finding the Frame Workshop.
Abstract
Goals play a central role in the study of agentic behavior. But what is a goal, and how should we best represent them? The traditional reinforcement learning answer is that all goals are expressible as the maximization of future rewards. While parsimonious, such a definition seems insufficient when viewed from both the perspective of humans specifying goals to machines and autotelic agents that self-propose tasks to explore and learn. We offer a critical perspective on the distillation of all goals directly into reward functions. We identify key features we believe goal representations ought to have, and then offer a proposal we believe meets those considerations.
Keywords
Bibtex entry:
@inproceedings{davidson2024goalsframe,
abstract = {Goals play a central role in the study of agentic behavior. But what is a goal, and how should we best represent them? The traditional reinforcement learning answer is that all goals are expressible as the maximization of future rewards. While parsimonious, such a definition seems insufficient when viewed from both the perspective of humans specifying goals to machines and autotelic agents that self-propose tasks to explore and learn. We offer a critical perspective on the distillation of all goals directly into reward functions. We identify key features we believe goal representations ought to have, and then offer a proposal we believe meets those considerations.},
author = {Davidson, G. and Gureckis, T.M.},
booktitle = {Reinforcement Learning Conference (RLC) Finding the Frame Workshop},
keywords = {RL, Creativity, Games, ArtificialIntelligence, Goals},
title = {Toward Complex and Structured Goals in Reinforcement Learning},
year = {2024}}QR Code:
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