# A Cognitive Framework for Autonomous Agents: Toward Human-Inspired Design

arXiv: https://arxiv.org/abs/2601.16648 · HTML: https://mlsanity.com/paper/2601.16648/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 8 of 8)

- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [When to Trust Your Model: Model-Based Policy Optimization](https://mlsanity.com/paper/1906.08253/) (2019)
- [Distributed Estimation with Information-Seeking Control in Agent Network](https://mlsanity.com/paper/1408.3732/) (2014)
- [Double Actor-Critic with TD Error-Driven Regularization in Reinforcement Learning](https://mlsanity.com/paper/2409.19231/) (2024)
- [Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach](https://mlsanity.com/paper/2001.04560/) (2020)
- [Networks of UAVs of Low-Complexity for Time-Critical Localization](https://mlsanity.com/paper/2108.13181/) (2021)
- [A Deep-NN Beamforming Approach for Dual Function Radar-Communication THz UAV](https://mlsanity.com/paper/2405.17015/) (2024)

## Abstract (arXiv)

This work introduces a human-inspired reinforcement learning (RL) architecture that integrates Pavlovian and instrumental processes to enhance decision-making in autonomous systems. While existing engineering solutions rely almost exclusively on instrumental learning, neuroscience shows that humans use Pavlovian associations to leverage predictive cues to bias behavior before outcomes occur. We translate this dual-system mechanism into a cue-guided RL framework in which radio-frequency (RF) stimuli act as conditioned (Pavlovian) cues that modulate action selection. The proposed architecture combines Pavlovian values with instrumental policy optimization, improving navigation efficiency and cooperative behavior in unknown, partially observable environments. Simulation results demonstrate that cue-driven agents adapt faster, achieving superior performance compared to traditional instrumental-solo agents. This work highlights the potential of human learning principles to reshape digital agents intelligence.