# Using Deep Q-Learning to Dynamically Toggle between Push/Pull Actions in Computational Trust Mechanisms

arXiv: https://arxiv.org/abs/2404.18296 · HTML: https://mlsanity.com/paper/2404.18296/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 28 Apr 2024.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 1 of 1)

- [A biologically inspired computational trust model for open multi-agent systems which is resilient to trustor population changes](https://mlsanity.com/paper/2404.10014/) (2024)

## Built on by (most cited first, 1 of 1)

- [A biologically Inspired Trust Model for Open Multi-Agent Systems that is Resilient to Rapid Performance Fluctuations](https://mlsanity.com/paper/2504.15301/) (2025)

## Abstract (arXiv)

Recent work on decentralized computational trust models for open Multi Agent Systems has resulted in the development of CA, a biologically inspired model which focuses on the trustee's perspective. This new model addresses a serious unresolved problem in existing trust and reputation models, namely the inability to handle constantly changing behaviors and agents' continuous entry and exit from the system. In previous work, we compared CA to FIRE, a well-known trust and reputation model, and found that CA is superior when the trustor population changes, whereas FIRE is more resilient to the trustee population changes. Thus, in this paper, we investigate how the trustors can detect the presence of several dynamic factors in their environment and then decide which trust model to employ in order to maximize utility. We frame this problem as a machine learning problem in a partially observable environment, where the presence of several dynamic factors is not known to the trustor and we describe how an adaptable trustor can rely on a few measurable features so as to assess the current state of the environment and then use Deep Q Learning (DQN), in a single-agent Reinforcement Learning setting, to learn how to adapt to a changing environment. We ran a series of simulation experiments to compare the performance of the adaptable trustor with the performance of trustors using only one model (FIRE or CA) and we show that an adaptable agent is indeed capable of learning when to use each model and, thus, perform consistently in dynamic environments.