# Learning What to Do and What Not To Do: Offline Imitation from Expert and Undesirable Demonstrations

arXiv: https://arxiv.org/abs/2505.21182 · HTML: https://mlsanity.com/paper/2505.21182/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 34 of its 44 references that are in the map.
- First posted on arXiv 27 May 2025.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 30 of 34)

- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations](https://mlsanity.com/paper/1904.06387/) (2019)
- [IQ-Learn: Inverse soft-Q Learning for Imitation](https://mlsanity.com/paper/2106.12142/) (2021)
- [AlgaeDICE: Policy Gradient from Arbitrary Experience](https://mlsanity.com/paper/1912.02074/) (2019)
- [Imitation Learning via Off-Policy Distribution Matching](https://mlsanity.com/paper/1912.05032/) (2019)
- [OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation](https://mlsanity.com/paper/2106.10783/) (2021)
- [The Ingredients of Real-World Robotic Reinforcement Learning](https://mlsanity.com/paper/2004.12570/) (2020)
- [Extreme Q-Learning: MaxEnt RL without Entropy](https://mlsanity.com/paper/2301.02328/) (2023)
- [Imitation Learning from Imperfect Demonstration](https://mlsanity.com/paper/1901.09387/) (2019)
- [Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations](https://mlsanity.com/paper/2207.10050/) (2022)
- [Preference Transformer: Modeling Human Preferences using Transformers for RL](https://mlsanity.com/paper/2303.00957/) (2023)
- [Inverse Preference Learning: Preference-based RL without a Reward Function](https://mlsanity.com/paper/2305.15363/) (2023)
- [Dual RL: Unification and New Methods for Reinforcement and Imitation Learning](https://mlsanity.com/paper/2302.08560/) (2023)
- [Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality](https://mlsanity.com/paper/2110.14754/) (2021)
- [Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching](https://mlsanity.com/paper/2202.02433/) (2022)
- [Learning Multimodal Rewards from Rankings](https://mlsanity.com/paper/2109.12750/) (2021)
- [Beyond Reward: Offline Preference-guided Policy Optimization](https://mlsanity.com/paper/2305.16217/) (2023)
- [LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning](https://mlsanity.com/paper/2303.00599/) (2023)
- [LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation](https://mlsanity.com/paper/2202.13536/) (2022)
- [ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update](https://mlsanity.com/paper/2402.00348/) (2024)
- [Non-Adversarial Imitation Learning and its Connections to Adversarial Methods](https://mlsanity.com/paper/2008.03525/) (2020)
- [Learning from negative feedback, or positive feedback or both](https://mlsanity.com/paper/2410.04166/) (2024)
- [How to Leverage Diverse Demonstrations in Offline Imitation Learning](https://mlsanity.com/paper/2405.17476/) (2024)

## Abstract (arXiv)

Offline imitation learning typically learns from expert and unlabeled demonstrations, yet often overlooks the valuable signal in explicitly undesirable behaviors. In this work, we study offline imitation learning from contrasting behaviors, where the dataset contains both expert and undesirable demonstrations. We propose a novel formulation that optimizes a difference of KL divergences over the state-action visitation distributions of expert and undesirable (or bad) data. Although the resulting objective is a DC (Difference-of-Convex) program, we prove that it becomes convex when expert demonstrations outweigh undesirable demonstrations, enabling a practical and stable non-adversarial training objective. Our method avoids adversarial training and handles both positive and negative demonstrations in a unified framework. Extensive experiments on standard offline imitation learning benchmarks demonstrate that our approach consistently outperforms state-of-the-art baselines.