mlsanityData to 7 Oct 2026

Generating Personas for Games with Multimodal Adversarial Imitation Learning

arXiv 2308.07598 ↗First posted 15 Aug 2023cs.LG cs.AIcited by 3

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

Reinforcement learning has been widely successful in producing agents capable of playing games at a human level. However, this requires complex reward engineering, and the agent's resulting policy is often unpredictable. Going beyond reinforcement learning is necessary to model a wide range of human playstyles, which can be difficult to represent with a reward function. This paper presents a novel imitation learning approach to generate multiple persona policies for playtesting. Multimodal Generative Adversarial Imitation Learning (MultiGAIL) uses an auxiliary input parameter to learn distinct personas using a single-agent model. MultiGAIL is based on generative adversarial imitation learning and uses multiple discriminators as reward models, inferring the environment reward by comparing the agent and distinct expert policies. The reward from each discriminator is weighted according to the auxiliary input. Our experimental analysis demonstrates the effectiveness of our technique in two environments with continuous and discrete action spaces.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 19 of its 33 references that are in the map. First posted on arXiv 15 Aug 2023.

Builds on

19 of 33 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning15 Aug
  2. 2021A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Character Control21 May

Other references · 17

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2016Concrete Problems in AI Safetycited by 1,582
  5. 2016Generative Adversarial Imitation Learningcited by 1,576
  6. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  7. 2016Least Squares Generative Adversarial Networkscited by 1,000
  8. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 723
  9. 2016A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Modelscited by 167
  10. 2021Augmenting Automated Game Testing with Deep Reinforcement Learningcited by 38
  11. 2018Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristicscited by 28
  12. 2021Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agentscited by 28
5 more
  1. 2021MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminatorscited by 11
  2. 2019Automated Playtesting of Matching Tile Gamescited by 10
  3. 2022Towards Automated Video Game Testing: Still a Long Way to Gocited by 5
  4. 2021Policy Fusion for Adaptive and Customizable Reinforcement Learning Agentscited by 4
  5. 2022Configurable Agent With Reward As Input: A Play-Style Continuum Generationcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Real-Time Diffusion Policies for Games: Enhancing Consistency Policies with Q-Ensemblescited by 3
  2. 2025UBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviorscited by 0
  3. 2026Augmenting Game AI with Deep Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2018Multi-Agent Generative Adversarial Imitation Learning
  2. 2018Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
  3. 2019Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
  4. 2022Generative Personas That Behave and Experience Like Humans
  5. 2020Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets
  6. 2023MAAIP: Multi-Agent Adversarial Interaction Priors for imitation from fighting demonstrations for physics-based characters
  7. 2024Behavioural Cloning in VizDoom
  8. 2022Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games