Maximum Entropy Semi-Supervised Inverse Reinforcement Learning
On the map: Offline RL and imitation learning from data → Inverse reinforcement learning
Abstract (arXiv)
A popular approach to apprenticeship learning (AL) is to formulate it as an inverse reinforcement learning (IRL) problem. The MaxEnt-IRL algorithm successfully integrates the maximum entropy principle into IRL and unlike its predecessors, it resolves the ambiguity arising from the fact that a possibly large number of policies could match the expert's behavior. In this paper, we study an AL setting in which in addition to the expert's trajectories, a number of unsupervised trajectories is available. We introduce MESSI, a novel algorithm that combines MaxEnt-IRL with principles coming from semi-supervised learning. In particular, MESSI integrates the unsupervised data into the MaxEnt-IRL framework using a pairwise penalty on trajectories. Empirical results in a highway driving and grid-world problems indicate that MESSI is able to take advantage of the unsupervised trajectories and improve the performance of MaxEnt-IRL.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 0 of its 28 references that are in the map. First posted on arXiv 22 Apr 2026.
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- 2020Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving
- 2026Fast Rates for Inverse Reinforcement Learning
- 2021Inverse Reinforcement Learning with Explicit Policy Estimates
- 2025Apprenticeship learning with prior beliefs using inverse optimization