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From reinforcement learning to agency: Frameworks for understanding basal cognition
Gabriella Seifert1, Ava Sealander2, Sarah Marzen3
1Department of Physics, University of Colorado, Boulder, CO 80309, USA; W. M. Keck Science Department, Pitzer, Scripps, and Claremont McKenna College, Claremont, CA 91711, USA.
Bio Systems
|December 21, 2023
Summary
This study unifies biology and artificial intelligence by combining multiscale competencies and goal-directedness formalisms (TAME) with reinforcement learning (RL). This framework enhances understanding of both biological organisms and AI agents.
Area of Science:
- Computational Biology
- Artificial Intelligence
- Cognitive Science
Background:
- Organisms exhibit behaviors like play, exploration, and mimicry, raising questions about their purpose and goal-directedness.
- Existing approaches to understanding complex agents, such as biological formalisms and reinforcement learning, often operate independently.
- A dichotomy exists between viewing behavior as purposeless or solely goal-driven, which this research challenges.
Purpose of the Study:
- To unify disparate approaches for understanding complex agents, both biological and engineered.
- To integrate formalisms for multiscale competencies and goal-directedness in biology with reinforcement learning.
- To foster a symbiotic framework for advancing research in both biology and artificial intelligence.
Main Methods:
- Proposing a symbiotic framework that combines biological formalisms (e.g., TAME) with reinforcement learning (RL).
- Leveraging TAME's capability to describe lower-level organisms and minimal agents.
- Utilizing RL's focus on higher-level organisms and complex robots.
Main Results:
- The proposed framework offers a novel approach to understanding biological organisms and artificial agents.
- It bridges the gap between theories of biological behavior and artificial intelligence.
- Identifies new research questions at the intersection of biology and AI.
Conclusions:
- The unification of TAME and RL provides a powerful lens for studying complex agents.
- This integrated approach can deepen our understanding of biological systems and inspire new AI development.
- Future research programs are expected to emerge from this combined framework.
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