# Incorporating Cognitive Biases into Reinforcement Learning for Financial Decision-Making

arXiv: https://arxiv.org/abs/2601.08247 · HTML: https://mlsanity.com/paper/2601.08247/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 2 of 2)

- [Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review](https://mlsanity.com/paper/2106.00123/) (2021)
- [Estimating cognitive biases with attention-aware inverse planning](https://mlsanity.com/paper/2510.25951/) (2025)

## Abstract (arXiv)

Financial markets are influenced by human behavior that deviates from rationality due to cognitive biases. Traditional reinforcement learning (RL) models for financial decision-making assume rational agents, potentially overlooking the impact of psychological factors. This study integrates cognitive biases into RL frameworks for financial trading, hypothesizing that such models can exhibit human-like trading behavior and achieve better risk-adjusted returns than standard RL agents. We introduce biases, such as overconfidence and loss aversion, into reward structures and decision-making processes and evaluate their performance in simulated and real-world trading environments. Despite its inconclusive or negative results, this study provides insights into the challenges of incorporating human-like biases into RL, offering valuable lessons for developing robust financial AI systems.