# Decision-Making Among Bounded Rational Agents

arXiv: https://arxiv.org/abs/2210.08672 · HTML: https://mlsanity.com/paper/2210.08672/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 17 Oct 2022.

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

- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [Safe Reinforcement Learning with Model Uncertainty Estimates](https://mlsanity.com/paper/1810.08700/) (2018)
- [Hierarchical Game-Theoretic Planning for Autonomous Vehicles](https://mlsanity.com/paper/1810.05766/) (2018)
- [Stein Variational Model Predictive Control](https://mlsanity.com/paper/2011.07641/) (2020)
- [Stochastic Dynamic Games in Belief Space](https://mlsanity.com/paper/1909.06963/) (2019)
- [Information-Theoretic Bounded Rationality](https://mlsanity.com/paper/1512.06789/) (2015)
- [A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing](https://mlsanity.com/paper/1801.02302/) (2018)
- [Robust Control Under Uncertainty via Bounded Rationality and Differential Privacy](https://mlsanity.com/paper/2109.08262/) (2021)

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

- [Runtime Analysis of Evolutionary Algorithms for Multi-party Multi-objective Optimization](https://mlsanity.com/paper/2501.16336/) (2025)

## Abstract (arXiv)

When robots share the same workspace with other intelligent agents (e.g., other robots or humans), they must be able to reason about the behaviors of their neighboring agents while accomplishing the designated tasks. In practice, frequently, agents do not exhibit absolutely rational behavior due to their limited computational resources. Thus, predicting the optimal agent behaviors is undesirable (because it demands prohibitive computational resources) and undesirable (because the prediction may be wrong). Motivated by this observation, we remove the assumption of perfectly rational agents and propose incorporating the concept of bounded rationality from an information-theoretic view into the game-theoretic framework. This allows the robots to reason other agents' sub-optimal behaviors and act accordingly under their computational constraints. Specifically, bounded rationality directly models the agent's information processing ability, which is represented as the KL-divergence between nominal and optimized stochastic policies, and the solution to the bounded-optimal policy can be obtained by an efficient importance sampling approach. Using both simulated and real-world experiments in multi-robot navigation tasks, we demonstrate that the resulting framework allows the robots to reason about different levels of rational behaviors of other agents and compute a reasonable strategy under its computational constraint.