Hyperbolic Inverse Reinforcement Learning (Hyperbolic IRL) introduces a reward function that depends on the Hyperbolic distance between feature vectors and a learned parameter vector, offering an alternative to traditional linear or neural network approaches. The method matches state‑of‑the‑art performance on standard benchmarks, performs especially well with limited data and in dense environments, and uncovers complex, non‑separable reward structures in human navigation tasks. Hyperbolic IRL thus enhances both predictive accuracy and interpretability of inferred reward functions.
arXiv:2610.14041v1 Announce Type: cross Abstract: Inverse Reinforcement Learning (IRL) is classically solved using Lagrangian optimisation, by jointly optimising a primal-dual problem with gradient d...
arXiv:2605. 14599v2 Announce Type: replace-cross Abstract: We establish novel structural and statistical results for entropy-regularized min-max inverse reinforcement learning (Min-Max-IRL) in finite-horizon MDPs with Borel state and action spaces.
arXiv:2607. 08647v1 Announce Type: cross Abstract: As autonomous agents are increasingly deployed across diverse operational contexts, aligning their behavior with human intent demands reward functions that remain robust to such changes rather than overfitting to any single environment.
arXiv:2605. 11020v2 Announce Type: replace-cross Abstract: Inverse reinforcement learning (IRL) is typically formulated as maximizing entropy subject to matching the distribution of expert trajectories.
arXiv:2512.24407v2 Announce Type: replace Abstract: In many sequential decision-making problems, researchers observe actions but not the rewards that drive behavior, yet still wish to evaluate and co...
arXiv:2502. 18447v2 Announce Type: replace Abstract: Existing approaches to reward inference typically assume that humans provide demonstrations according to specific behavior models.
arXiv:2506. 09276v4 Announce Type: replace-cross Abstract: This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories, requiring neither reward signals nor the actions executed by the agent.
The paper introduces RADT, a decision transformer for offline, reward‑free, goal‑conditioned reinforcement learning that can handle arbitrary avoid‑regions specified at evaluation time. By encoding goals and avoid‑regions as prompt tokens and using an avoid‑region hindsight relabeling strategy, RADT learns reach‑avoid behavior from suboptimal random trajectories without reward or cost supervision. Across 17 tasks and environments, RADT outperforms existing offline goal‑conditioned RL models, achieving significant zero‑shot generalization to novel avoid‑region sizes and counts, and is also applied to a biological cell reprogramming task.
arXiv:2608. 11052v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL) aims to recover a reward function under which the resulting policy reproduces the behavior observed in expert demonstrations.
arXiv:2609.27572v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as a key paradigm for improving the reasoning capabilities of large language models (LLMs). However, existing...
Inverse reinforcement learning (IRL) provides a powerful framework for learning from demonstrations. However, real-world tasks often exhibit substantial natural variations (e.
arXiv:2610.19783v1 Announce Type: new Abstract: Reinforcement learning (RL) holds the promise of learning a policy that can outperform what is shown in the data. However, in practice, such systems su...