# Configurable Agent With Reward As Input: A Play-Style Continuum Generation

arXiv: https://arxiv.org/abs/2211.16221 · HTML: https://mlsanity.com/paper/2211.16221/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Sample Efficient Actor-Critic with Experience Replay](https://mlsanity.com/paper/1611.01224/) (2016)
- [Visualizing and Understanding Atari Agents](https://mlsanity.com/paper/1711.00138/) (2017)
- [Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristics](https://mlsanity.com/paper/1802.06881/) (2018)
- [Benchmarking End-to-End Behavioural Cloning on Video Games](https://mlsanity.com/paper/2004.00981/) (2020)

## Built on by (most cited first, 4 of 4)

- [Generating Personas for Games with Multimodal Adversarial Imitation Learning](https://mlsanity.com/paper/2308.07598/) (2023)
- [Diversity is Strength: Mastering Football Full Game with Interactive Reinforcement Learning of Multiple AIs](https://mlsanity.com/paper/2306.15903/) (2023)
- [UBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviors](https://mlsanity.com/paper/2512.10835/) (2025)
- [Coachable agents for interactive gameplay](https://mlsanity.com/paper/2607.00642/) (2026)

## Abstract (arXiv)

Modern video games are becoming richer and more complex in terms of game mechanics. This complexity allows for the emergence of a wide variety of ways to play the game across the players. From the point of view of the game designer, this means that one needs to anticipate a lot of different ways the game could be played. Machine Learning (ML) could help address this issue. More precisely, Reinforcement Learning is a promising answer to the need of automating video game testing. In this paper we present a video game environment which lets us define multiple play-styles. We then introduce CARI: a Configurable Agent with Reward as Input. An agent able to simulate a wide continuum range of play-styles. It is not constrained to extreme archetypal behaviors like current methods using reward shaping. In addition it achieves this through a single training loop, instead of the usual one loop per play-style. We compare this novel training approach with the more classic reward shaping approach and conclude that CARI can also outperform the baseline on archetypes generation. This novel agent could be used to investigate behaviors and balancing during the production of a video game with a realistic amount of training time.