arXiv research

Paper uses Apprenticeship Learning to model player behavior in interactive narratives.

MPC outperforms reactive budgeting in non-stationary return environments.

We study an adaptive source seeking problem, in which a mobile robot must identify the strongest emitter(s) of a signal in an environment with background emissions. Background signals may be highly heterogeneous and can mislead algorithms that are based on receding horizon control. We propose AdaSearch, a general algor…

Model learns and plans in real-time under constraints for robotic systems.

New algorithm optimizes exploration in unknown MDPs.

Study shows fast rates for inverse reinforcement learning with linear rewards.

We examine optimal execution models that take into account both market microstructure impact and informational costs. Informational footprint is related to order flow and is represented by the trader's influence on the flow imbalance process, while microstructure influence is captured by instantaneous price impact. We …

Improved algorithm for optimal stopping problems reduces runtime.

Efficient learning-based MPC for unknown nonlinear systems with state constraints.

Multi-task Inverse Reinforcement Learning (IRL) is the problem of inferring multiple reward functions from expert demonstrations. Prior work, built on Bayesian IRL, is unable to scale to complex environments due to computational constraints. This paper contributes a formulation of multi-task IRL in the more computation…

Paper proves IRLS converges to subspace from any start, with practical benefits.

Inverse reinforcement learning (IRL) is the problem of learning the preferences of an agent from the observations of its behavior on a task. While this problem has been well investigated, the related problem of {\em online} IRL---where the observations are incrementally accrued, yet the demands of the application often…

Paper proposes Langevin dynamics for adaptive IRL of stochastic gradient algorithms.

A new approach optimizes weights in DLP for better risk-adjusted performance.

New IRL algorithm identifies optimal reward and policy from expert demonstrations.

The paper solves IRL for Bayesian stopping time problems.

Inverse reinforcement learning (IRL) infers a reward function from demonstrations, allowing for policy improvement and generalization. However, despite much recent interest in IRL, little work has been done to understand the minimum set of demonstrations needed to teach a specific sequential decision-making task. We fo…

In a previous paper we obtained formulae for the volume of a causal diamond or Alexandrov open set $I^+(p) \cap I^-(q)$ whose duration $τ(p,q)$ is short compared with the curvature scale. In the present paper we obtain asymptotic formulae valid when the point $q$ recedes to the future boundary ${\cal I}^+$ of an asymp…

Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a problem and as a class of methods. By categorically surveying the current literature in IRL, this article serves as a reference for researc…

Proposes CWAE-IRL for efficient IRL in complex environments.

New algorithm reduces performance loss in IRL with mismatched transition dynamics.

Global convergence for robust regression problems via IRLS with enhancements.

New method learns multiple reward functions for complex tasks.

Mix-IRLS solves imbalanced mixed linear regression problems efficiently.

This work presents a general framework for solving the low rank and/or sparse matrix minimization problems, which may involve multiple non-smooth terms. The Iteratively Reweighted Least Squares (IRLS) method is a fast solver, which smooths the objective function and minimizes it by alternately updating the variables an…

The goal of the inverse reinforcement learning (IRL) problem is to recover the reward functions from expert demonstrations. However, the IRL problem like any ill-posed inverse problem suffers the congenital defect that the policy may be optimal for many reward functions, and expert demonstrations may be optimal for man…

Active learning improves RS-IRL by querying expert demonstrations to uncover risk boundaries.

Time dilation $\frac{1}{\sqrt{1-v^2}}$ and relative velocity $v$ are observationally indistinguishable in the special theory of relativity, a duality that carries over into the general theory under Fermi coordinates along a curve (in coordinate-independent language, in the tangent Minkowski space along the curve). For …

Paper tackles efficient IRL in offline settings with polynomial samples and runtime.

This work simplifies IRL by using potential-based reward shaping.

AceIRL learns expert reward from active exploration.

New IRL algorithm for continuous state spaces with formal guarantees.

Examines WENDy-IRLS algorithm's noise robustness and efficiency in various differential equations.

Paper analyzes PSGLD for adaptive IRL with finite-sample bounds.

In this paper we present a connection between two dynamical systems arising in entirely different contexts: one in signal processing and the other in biology. The first is the famous Iteratively Reweighted Least Squares (IRLS) algorithm used in compressed sensing and sparse recovery while the second is the dynamics of …

EBIL simplifies IL by estimating expert energy as reward, achieving effective performance.

Novel algorithm reduces computational burden in IRL with finite-time guarantees.

A new IRL model recovers reward and state structure from expert demonstrations.

Inverse reinforcement learning (IRL) has become a useful tool for learning behavioral models from demonstration data. However, IRL remains mostly unexplored for multi-agent systems. In this paper, we show how the principle of IRL can be extended to homogeneous large-scale problems, inspired by the collective swarming b…

New approach uses inverse reinforcement learning to improve language model training.

Unified probabilistic perspective on imitation learning methods using divergence minimization.

Optimal Biweight kernel and computationally efficient Epanechnikov kernel for modal linear regression.

Novel IRL method identifies suboptimal medical decisions in ICU data.

Inverse reinforcement learning (IRL) is the problem of finding a reward function that generates a given optimal policy for a given Markov Decision Process. This paper looks at an algorithmic-independent geometric analysis of the IRL problem with finite states and actions. A L1-regularized Support Vector Machine formula…

We model human decision-making behaviors in a risk-taking task using inverse reinforcement learning (IRL) for the purposes of understanding real human decision making under risk. To the best of our knowledge, this is the first work applying IRL to reveal the implicit reward function in human risk-taking decision making…

This paper sets a lower bound for sample complexity in inverse reinforcement learning.

Advances in the field of inverse reinforcement learning (IRL) have led to sophisticated inference frameworks that relax the original modeling assumption of observing an agent behavior that reflects only a single intention. Instead of learning a global behavioral model, recent IRL methods divide the demonstration data i…

Text generation is a crucial task in NLP. Recently, several adversarial generative models have been proposed to improve the exposure bias problem in text generation. Though these models gain great success, they still suffer from the problems of reward sparsity and mode collapse. In order to address these two problems, …