# Introducing Neuromodulation in Deep Neural Networks to Learn Adaptive Behaviours

## TL;DR

This paper introduces a neuromodulation-inspired architecture for deep neural networks that enhances their ability to learn and adapt to changing environments, inspired by biological cellular neuromodulation mechanisms.

## Contribution

The paper proposes a novel neuromodulation-based neural network architecture specifically designed for adaptive behavior learning in dynamic environments.

## Key findings

- Neuromodulation improves task adaptation in navigation benchmarks.
- The approach outperforms state-of-the-art methods in meta-reinforcement learning.
- Neuromodulation enhances the flexibility and robustness of artificial agents.

## Abstract

Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack. Such an adaptation property relies heavily on cellular neuromodulation, the biological mechanism that dynamically controls intrinsic properties of neurons and their response to external stimuli in a context-dependent manner. In this paper, we take inspiration from cellular neuromodulation to construct a new deep neural network architecture that is specifically designed to learn adaptive behaviours. The network adaptation capabilities are tested on navigation benchmarks in a meta-reinforcement learning context and compared with state-of-the-art approaches. Results show that neuromodulation is capable of adapting an agent to different tasks and that neuromodulation-based approaches provide a promising way of improving adaptation of artificial systems.