# TROFI: Trajectory-Ranked Offline Inverse Reinforcement Learning

arXiv: https://arxiv.org/abs/2506.22008 · HTML: https://mlsanity.com/paper/2506.22008/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 29 of its 39 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Placed in

- Offline RL and imitation learning from data / [Inverse reinforcement learning](https://mlsanity.com/offlinerl/inverse-reinforcement-learning/)

## Builds on (most cited first, 29 of 29)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [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)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning](https://mlsanity.com/paper/1910.00177/) (2019)
- [Imitating Human Behaviour with Diffusion Models](https://mlsanity.com/paper/2301.10677/) (2023)
- [Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations](https://mlsanity.com/paper/1904.06387/) (2019)
- [Offline Reinforcement Learning with Fisher Divergence Critic Regularization](https://mlsanity.com/paper/2103.08050/) (2021)
- [Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations](https://mlsanity.com/paper/2207.10050/) (2022)
- [Offline Learning from Demonstrations and Unlabeled Experience](https://mlsanity.com/paper/2011.13885/) (2020)
- [Inverse Preference Learning: Preference-based RL without a Reward Function](https://mlsanity.com/paper/2305.15363/) (2023)
- [How to Leverage Unlabeled Data in Offline Reinforcement Learning](https://mlsanity.com/paper/2202.01741/) (2022)
- [Optimal Transport for Offline Imitation Learning](https://mlsanity.com/paper/2303.13971/) (2023)
- [Direct Preference-based Policy Optimization without Reward Modeling](https://mlsanity.com/paper/2301.12842/) (2023)
- [Beyond Reward: Offline Preference-guided Policy Optimization](https://mlsanity.com/paper/2305.16217/) (2023)
- [Semi-supervised reward learning for offline reinforcement learning](https://mlsanity.com/paper/2012.06899/) (2020)
- [Survival Instinct in Offline Reinforcement Learning](https://mlsanity.com/paper/2306.03286/) (2023)
- [MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations](https://mlsanity.com/paper/2303.17156/) (2023)
- ["It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games](https://mlsanity.com/paper/2009.00541/) (2020)
- [Winning Isn't Everything: Enhancing Game Development with Intelligent Agents](https://mlsanity.com/paper/1903.10545/) (2019)
- [Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning](https://mlsanity.com/paper/2208.07811/) (2022)
- [Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games](https://mlsanity.com/paper/2307.11105/) (2023)
- [The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning](https://mlsanity.com/paper/2302.13493/) (2023)
- [Efficient Active Imitation Learning with Random Network Distillation](https://mlsanity.com/paper/2411.01894/) (2024)
- [Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments](https://mlsanity.com/paper/2012.02527/) (2020)
- [Real-Time Diffusion Policies for Games: Enhancing Consistency Policies with Q-Ensembles](https://mlsanity.com/paper/2503.16978/) (2025)

## Abstract (arXiv)

In offline reinforcement learning, agents are trained using only a fixed set of stored transitions derived from a source policy. However, this requires that the dataset be labeled by a reward function. In applied settings such as video game development, the availability of the reward function is not always guaranteed. This paper proposes Trajectory-Ranked OFfline Inverse reinforcement learning (TROFI), a novel approach to effectively learn a policy offline without a pre-defined reward function. TROFI first learns a reward function from human preferences, which it then uses to label the original dataset making it usable for training the policy. In contrast to other approaches, our method does not require optimal trajectories. Through experiments on the D4RL benchmark we demonstrate that TROFI consistently outperforms baselines and performs comparably to using the ground truth reward to learn policies. Additionally, we validate the efficacy of our method in a 3D game environment. Our studies of the reward model highlight the importance of the reward function in this setting: we show that to ensure the alignment of a value function to the actual future discounted reward, it is fundamental to have a well-engineered and easy-to-learn reward function.