# Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learning

arXiv: https://arxiv.org/abs/2208.14037 · HTML: https://mlsanity.com/paper/2208.14037/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 14 of 14)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Reinforcement Learning with Deep Energy-Based Policies](https://mlsanity.com/paper/1702.08165/) (2017)
- [Risk-Constrained Reinforcement Learning with Percentile Risk Criteria](https://mlsanity.com/paper/1512.01629/) (2015)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [Explainability in Deep Reinforcement Learning](https://mlsanity.com/paper/2008.06693/) (2020)
- [A survey on intrinsic motivation in reinforcement learning](https://mlsanity.com/paper/1908.06976/) (2019)
- [Reinforcement Learning with Convex Constraints](https://mlsanity.com/paper/1906.09323/) (2019)
- [Information asymmetry in KL-regularized RL](https://mlsanity.com/paper/1905.01240/) (2019)
- [Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution](https://mlsanity.com/paper/2009.14108/) (2020)
- [Implementations in Machine Ethics: A Survey](https://mlsanity.com/paper/2001.07573/) (2020)
- [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)
- [Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?](https://mlsanity.com/paper/2202.12174/) (2022)

## Built on by (most cited first, 3 of 3)

- [Reinforcement Learning and Machine ethics:a systematic review](https://mlsanity.com/paper/2407.02425/) (2024)
- [Toward Virtuous Reinforcement Learning: A Critique and Roadmap](https://mlsanity.com/paper/2512.04246/) (2025)
- [Fog of Love: Engineering Virtuous Agent Behavior with Affinity-based Reinforcement Learning in a Game Environment](https://mlsanity.com/paper/2606.04750/) (2026)

## Abstract (arXiv)

Machine ethics has received increasing attention over the past few years because of the need to ensure safe and reliable artificial intelligence (AI). The two dominantly used theories in machine ethics are deontological and utilitarian ethics. Virtue ethics, on the other hand, has often been mentioned as an alternative ethical theory. While this interesting approach has certain advantages over popular ethical theories, little effort has been put into engineering artificial virtuous agents due to challenges in their formalization, codifiability, and the resolution of ethical dilemmas to train virtuous agents. We propose to bridge this gap by using role-playing games riddled with moral dilemmas. There are several such games in existence, such as Papers, Please and Life is Strange, where the main character encounters situations where they must choose the right course of action by giving up something else dear to them. We draw inspiration from such games to show how a systemic role-playing game can be designed to develop virtues within an artificial agent. Using modern day AI techniques, such as affinity-based reinforcement learning and explainable AI, we motivate the implementation of virtuous agents that play such role-playing games, and the examination of their decisions through a virtue ethical lens. The development of such agents and environments is a first step towards practically formalizing and demonstrating the value of virtue ethics in the development of ethical agents.