# Fog of Love: Engineering Virtuous Agent Behavior with Affinity-based Reinforcement Learning in a Game Environment

arXiv: https://arxiv.org/abs/2606.04750 · HTML: https://mlsanity.com/paper/2606.04750/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## 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, 15 of 15)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Defining and Characterizing Reward Hacking](https://mlsanity.com/paper/2209.13085/) (2022)
- [Behavior Priors for Efficient Reinforcement Learning](https://mlsanity.com/paper/2010.14274/) (2020)
- [Policy Regularization with Dataset Constraint for Offline Reinforcement Learning](https://mlsanity.com/paper/2306.06569/) (2023)
- [MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning](https://mlsanity.com/paper/2201.00012/) (2021)
- [Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.14344/) (2022)
- [Toward the Engineering of Virtuous Machines](https://mlsanity.com/paper/1812.03868/) (2018)
- [Reinforcement Learning Your Way: Agent Characterization through Policy Regularization](https://mlsanity.com/paper/2201.10003/) (2022)
- [Can Interpretable Reinforcement Learning Manage Prosperity Your Way?](https://mlsanity.com/paper/2202.09064/) (2022)
- [The Virtuous Machine - Old Ethics for New Technology?](https://mlsanity.com/paper/1806.10322/) (2018)
- [Policy Regularization for Legible Behavior](https://mlsanity.com/paper/2203.04303/) (2022)
- [Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learning](https://mlsanity.com/paper/2208.14037/) (2022)
- [Artificial virtuous agents in a multiagent tragedy of the commons](https://mlsanity.com/paper/2210.02769/) (2022)

## Abstract (arXiv)

Instilling virtuous behavior in artificial intelligence has seen increasing interest. One of the techniques proposed is known as affinity-based reinforcement learning, which uses policy regularization on the objective function to incentivize virtuous actions without being fully dependent on the reward function design. Thus far, this technique has been demonstrated to be effective in grid worlds and toy-problem environments with minimal state and action spaces. To expand this research to more sophisticated environments, we introduce a two-player multi-agent environment based on the role-playing board game known as Fog of Love. In this environment, two agents compete to fulfill their individual virtues, while also cooperating to satisfy their relationship. Given the multi-agent nature, this is a complex problem where multi-agent deep deterministic policy gradient agents neither compete nor cooperate successfully. We present evidence that localized affinities enhance agent performance in achieving both competitive and cooperative objectives, resulting from superior overall scores in both domains. This not only results in virtuous choices but also clarifies an agent's teleology and makes its behavior human-level interpretable.