inverse-reinforcement-learning

This paper recasts preference-based reward learning as a human-autonomy team problem, arguing that a teacher who knows the objective can design more efficient training examples than learner-driven query selection. It introduces understanding statements with second-order theory-of-mind to keep the teacher's model of the learner synchronized, showing in simulation that this approach outperforms learner-led selection.

The paper proposes an adversarial inverse reinforcement learning framework for machinery fault detection that learns a health reward from normal operational data without requiring fault labels, achieving consistent detection across multiple benchmarks.

This paper presents a nonparametric Bayesian inverse reinforcement learning approach using a Dirichlet process prior to infer multiple latent reward types from expert demonstrations, implementing a collapsed Gibbs sampler with parallelization via Ray for scalability.

This paper proposes a framework linking partially observable Markov decision processes (POMDPs) with biochemical reaction dynamics to model phototaxis in unicellular algae, using inverse reinforcement learning to infer behavioral objectives from experimental trajectories.

This paper introduces GRID, a social learning method that extracts universal behaviors from heterogeneous agents by decomposing per-agent reward functions into general and specific rewards, enabling a generalist agent that avoids mode-averaging bias.

This paper identifies the problem of missing observations in inverse reinforcement learning (IRL) that can make expert actions appear suboptimal, and develops a practical algorithm to quantify the minimal perturbations needed for expert actions to appear optimal, validated on synthetic tasks, cancer treatment simulation, and ICU data.

This paper introduces Trust Region Inverse Reinforcement Learning (TRIRL), a method that combines monotonic dual improvement with efficient local policy updates to outperform state-of-the-art imitation learning methods. It addresses the trade-off between stability and computational cost in IRL by using trust-region constraints.

This paper introduces Interactive Inverse Reinforcement Learning (IIRL), a framework where a learner actively interacts with an expert to infer reward functions, formulated as a stochastic bi-level optimization problem. The authors propose the BISIRL algorithm, providing convergence guarantees and experimental validation for this interactive learning paradigm.

This paper introduces MOCI, a novel framework for inferring shared constraints and individual preferences from heterogeneous expert demonstrations in reinforcement learning, outperforming existing baselines in predictive performance and computational efficiency.

Researchers propose SPS (Steering Probability Squeezing), a training paradigm combining reinforcement learning with inverse reinforcement learning to address probability squeezing in LLM reasoning training, where probability mass concentrates too narrowly on high-reward trajectories, limiting exploration and multi-sample performance (Pass@k). Experiments on five reasoning benchmarks demonstrate improved exploration and Pass@k metrics.

This paper establishes mathematical equivalences between generative adversarial networks (GANs), inverse reinforcement learning (IRL), and energy-based models (EBMs), demonstrating that certain IRL methods are equivalent to GANs with evaluable generator density. The work bridges three research communities to enable knowledge transfer for developing more stable and scalable algorithms.