Evolution of Interpretable Learning Rules in Foraging Agents

Core Concepts

Optimizing plasticity rules for learning in embodied agents leads to interpretable and adaptable behaviors.

Abstract

The content explores the evolution of plasticity rules in foraging agents through meta-learning. It discusses the impact of network structure, task parameters, regularization, information bottlenecks, and weight normalization on the development of learning rules. The study highlights how different objective functions and nonlinearity affect the evolved rules. Additionally, it delves into the comparison between static and moving agents' learning processes. I. Introduction Living organisms' ability to adapt and learn. Importance of synaptic plasticity in biological organisms. Meta-learning via evolutionary optimization for training artificial neural networks. II. Methods Environment setup with food particles. Agent's motor network and plastic sensory network description. Plasticity rule parametrization using reward-modulated mechanism. Evolutionary algorithm details. III. Summary of Previous Results Effect of environmental factors on evolved learning rate. Impact of network parameters on plasticity rules. IV. Results A. Evolution to solve foraging task: Convergence of sensory weights for accurate navigation. B. Redundancy in plasticity rules: Reduction through information bottleneck. C. Information bottleneck improves performance: Generalizability observed with binary readouts. D. Regularization leads to interpretable rules: Minimal rule patterns developed with regularization techniques. E. Weight normalization impacts evolved learning rule: Divisive normalization affects convergence patterns. F. Trainable nonlinearity on sensory readout: Steepness influences evolved sigmoid functions. G. Static agents: Distribution affects emerging rule: Comparison between static and moving agent's learning processes. H. Different objective functions lead to different evolved rules: Prediction vs decision loss impact on plasticity rule evolution. V. Discussion Plasticity optimization for understanding biological learning processes and building autonomous systems. Impact of nonlinearity, weight normalization, and objective function on evolved learning rules. VI. Acknowledgements Acknowledgment of funding sources supporting the research.

Stats

Developing reliable mechanisms for continuous local learning is a central challenge faced by biological and artificial systems. Synaptic plasticity is observed across a wide variety of organisms and remains active throughout most organisms’ lifetimes.

Quotes

"Reward-modulated plasticity has been extensively studied as a plausible mechanism for credit assignment in the brain." "Small changes in neuronal nonlinearity or weight normalization can strongly affect the evolutionary trajectory of reward-modulated plasticity rules."

Key Insights Distilled From

Network bottlenecks and task structure control the evolution of interpretable learning rules in a foraging agent

by Emmanouil Gi... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13649.pdf

Deeper Inquiries

How can these findings be applied to enhance machine learning algorithms?

The findings from this study on the evolution of plasticity rules in embodied agents can be applied to enhance machine learning algorithms in several ways. Firstly, by understanding how different environmental factors and structural constraints influence the optimal plasticity mechanisms, researchers can develop more adaptive and robust learning algorithms. By incorporating regularization techniques and information bottlenecks into the training process, variability in evolved learning rules can be reduced, leading to more interpretable and stable models. Additionally, the study highlights the sensitivity of meta-learning of plasticity rules to various parameters. This insight can guide the development of more efficient optimization methods for training artificial neural networks. By considering network structure, task complexity, and evolutionary processes during meta-learning, researchers can improve the performance and generalizability of machine learning algorithms.

What are potential implications for understanding human brain function?

The research on evolving interpretable learning rules in foraging agents has significant implications for understanding human brain function. The study demonstrates how simple reward-modulated plasticity rules can lead to complex behaviors such as decision-making based on sensory inputs. This mirrors synaptic plasticity mechanisms observed in biological organisms where changes in synaptic strengths between neurons play a crucial role in learning and memory formation. By studying how different factors influence the evolution of plasticity rules in artificial neural networks, researchers gain insights into potential objective functions that drive biological learning processes. Understanding these underlying principles could shed light on how humans adapt to their environment, assimilate new information, and modify behavior over time.

How might these results influence future studies on artificial intelligence?

These results have several implications for future studies on artificial intelligence (AI). Firstly, they highlight the importance of considering environmental factors and structural constraints when designing AI systems that learn continuously from their surroundings. By optimizing reward-modulated plasticity rules through evolutionary computation methods like meta-learning, AI systems can become more adaptive and autonomous. Furthermore, the findings suggest that introducing regularization techniques and information bottlenecks can help reduce variability in evolved learning rules, leading to more interpretable models. This approach could be valuable for developing AI systems with improved stability, performance, and generalization capabilities. Overall, these results pave the way for further research into enhancing AI algorithms through an understanding of evolutionary optimization strategies and their impact on model interpretability and robustness