# Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation

arXiv: https://arxiv.org/abs/2001.00503 · HTML: https://mlsanity.com/paper/2001.00503/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2020.

## Built on by (most cited first, 19 of 19)

- [The Utility of Explainable AI in Ad Hoc Human-Machine Teaming](https://mlsanity.com/paper/2209.03943/) (2022)
- [Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations](https://mlsanity.com/paper/2209.11908/) (2022)
- [Robotic Table Tennis: A Case Study into a High Speed Learning System](https://mlsanity.com/paper/2309.03315/) (2023)
- [Inverse Reinforcement Learning by Estimating Expertise of Demonstrators](https://mlsanity.com/paper/2402.01886/) (2024)
- [ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics](https://mlsanity.com/paper/2411.18825/) (2024)
- [Leveraging Large Language Models in Human-Robot Interaction: A Critical Analysis of Potential and Pitfalls](https://mlsanity.com/paper/2405.00693/) (2024)
- [ARCADE: Scalable Demonstration Collection and Generation via Augmented Reality for Imitation Learning](https://mlsanity.com/paper/2410.15994/) (2024)
- [Multi-Camera Asynchronous Ball Localization and Trajectory Prediction with Factor Graphs and Human Poses](https://mlsanity.com/paper/2401.17185/) (2024)
- [Safe Inverse Reinforcement Learning via Control Barrier Function](https://mlsanity.com/paper/2212.02753/) (2022)
- [LiMIIRL: Lightweight Multiple-Intent Inverse Reinforcement Learning](https://mlsanity.com/paper/2106.01777/) (2021)
- [Towards Sample-efficient Apprenticeship Learning from Suboptimal Demonstration](https://mlsanity.com/paper/2110.04347/) (2021)
- [Strategy Discovery and Mixture in Lifelong Learning from Heterogeneous Demonstration](https://mlsanity.com/paper/2202.07014/) (2022)
- [Multi Task Inverse Reinforcement Learning for Common Sense Reward](https://mlsanity.com/paper/2402.11367/) (2024)
- [Imitation Learning with Additional Constraints on Motion Style using Parametric Bias](https://mlsanity.com/paper/2407.08057/) (2024)
- [Augmented Reality Demonstrations for Scalable Robot Imitation Learning](https://mlsanity.com/paper/2403.13910/) (2024)
- [Differentiable Belief-based Opponent Shaping](https://mlsanity.com/paper/2605.29042/) (2026)
- [Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach](https://mlsanity.com/paper/2605.30903/) (2026)
- [ConTraIRL: Factorized Contrastive Abstractions for Transferable IRL](https://mlsanity.com/paper/2606.03017/) (2026)
- [Do as the Romans Do: Learning Universal Behaviors from Heterogeneous Agents](https://mlsanity.com/paper/2606.18537/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) has achieved tremendous success as a general framework for learning how to make decisions. However, this success relies on the interactive hand-tuning of a reward function by RL experts. On the other hand, inverse reinforcement learning (IRL) seeks to learn a reward function from readily-obtained human demonstrations. Yet, IRL suffers from two major limitations: 1) reward ambiguity - there are an infinite number of possible reward functions that could explain an expert's demonstration and 2) heterogeneity - human experts adopt varying strategies and preferences, which makes learning from multiple demonstrators difficult due to the common assumption that demonstrators seeks to maximize the same reward. In this work, we propose a method to jointly infer a task goal and humans' strategic preferences via network distillation. This approach enables us to distill a robust task reward (addressing reward ambiguity) and to model each strategy's objective (handling heterogeneity). We demonstrate our algorithm can better recover task reward and strategy rewards and imitate the strategies in two simulated tasks and a real-world table tennis task.