# Reinforcement Learning and Machine ethics:a systematic review

arXiv: https://arxiv.org/abs/2407.02425 · HTML: https://mlsanity.com/paper/2407.02425/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods.
- Builds on 21 of its 93 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Placed in

- Deep reinforcement learning methods / [RL training of language and agent models](https://mlsanity.com/deeprl/rl-training-of-language-and-agent-models/)

## Builds on (most cited first, 21 of 21)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark](https://mlsanity.com/paper/2304.03279/) (2023)
- [Pragmatic-Pedagogic Value Alignment](https://mlsanity.com/paper/1707.06354/) (2017)
- [Aligning to Social Norms and Values in Interactive Narratives](https://mlsanity.com/paper/2205.01975/) (2022)
- [MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning](https://mlsanity.com/paper/2201.00012/) (2021)
- [Building Ethics into Artificial Intelligence](https://mlsanity.com/paper/1812.02953/) (2018)
- [A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents](https://mlsanity.com/paper/1712.04172/) (2017)
- [Incorporating Behavioral Constraints in Online AI Systems](https://mlsanity.com/paper/1809.05720/) (2018)
- [A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors](https://mlsanity.com/paper/2302.13137/) (2023)
- [Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior](https://mlsanity.com/paper/2104.09469/) (2021)
- [Landscape of Machine Implemented Ethics](https://mlsanity.com/paper/2009.00335/) (2020)
- [Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2301.08491/) (2023)
- [Norm Conflict Resolution in Stochastic Domains](https://mlsanity.com/paper/1706.07448/) (2017)
- [The Jiminy Advisor: Moral Agreements Among Stakeholders Based on Norms and Argumentation](https://mlsanity.com/paper/1812.04741/) (2018)
- [Teaching Social Behavior through Human Reinforcement for Ad hoc Teamwork -The STAR Framework](https://mlsanity.com/paper/1809.07880/) (2018)
- [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)
- [Learning Not to Spoof](https://mlsanity.com/paper/2306.06087/) (2023)
- [Moral reinforcement learning using actual causation](https://mlsanity.com/paper/2205.08192/) (2022)

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

- [MoralityGym: A Benchmark for Evaluating Hierarchical Moral Alignment in Sequential Decision-Making Agents](https://mlsanity.com/paper/2602.13372/) (2026)
- [Acting for the Right Reasons: Creating Reason-Sensitive Artificial Moral Agents](https://mlsanity.com/paper/2409.15014/) (2024)
- [Toward Virtuous Reinforcement Learning: A Critique and Roadmap](https://mlsanity.com/paper/2512.04246/) (2025)

## Abstract (arXiv)

Machine ethics is the field that studies how ethical behaviour can be accomplished by autonomous systems. While there exist some systematic reviews aiming to consolidate the state of the art in machine ethics prior to 2020, these tend to not include work that uses reinforcement learning agents as entities whose ethical behaviour is to be achieved. The reason for this is that only in the last years we have witnessed an increase in machine ethics studies within reinforcement learning. We present here a systematic review of reinforcement learning for machine ethics and machine ethics within reinforcement learning. Additionally, we highlight trends in terms of ethics specifications, components and frameworks of reinforcement learning, and environments used to result in ethical behaviour. Our systematic review aims to consolidate the work in machine ethics and reinforcement learning thus completing the gap in the state of the art machine ethics landscape