A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper solves IRL for Bayesian stopping time problems.
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 proposes Langevin dynamics for adaptive IRL of stochastic gradient algorithms.
B-REX efficiently learns Atari game policies from pixel inputs using Bayesian methods.
Bayesian Robust Optimization for Imitation Learning (BROIL) balances risk and reward.
Proposes a real-time anomaly detection system using IRL.
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…
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…
New IRL algorithm identifies optimal reward and policy from expert demonstrations.
We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations. Our model stacks multiple latent GP layers to learn abstract representations of the state feature space, which is link…
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…
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…
Paper tackles efficient IRL in offline settings with polynomial samples and runtime.
One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution. This assumption deviates from actual human behaviors under ambiguity. Risk-sensitive inverse reinforcement learning (RS-IRL) bridges such gap by assum…
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.
Multi-agent learning is a promising method to simulate aggregate competitive behaviour in finance. Learning expert agents' reward functions through their external demonstrations is hence particularly relevant for subsequent design of realistic agent-based simulations. Inverse Reinforcement Learning (IRL) aims at acquir…
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 …
Framework uses IRL and RL to elicit and optimize risk preferences robustly to noise.
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.
Study shows fast rates for inverse reinforcement learning with linear rewards.
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.
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, …
We study the problem of inverse reinforcement learning (IRL) with the added twist that the learner is assisted by a helpful teacher. More formally, we tackle the following algorithmic question: How could a teacher provide an informative sequence of demonstrations to an IRL learner to speed up the learning process? We p…
Iteratively reweighted least squares (IRLS) is a widely-used method in machine learning to estimate the parameters in the generalised linear models. In particular, IRLS for L1 minimisation under the linear model provides a closed-form solution in each step, which is a simple multiplication between the inverse of the we…
Active IRL selects optimal human demonstrations for learning AI preferences.
A new method solves sparse regularization problems efficiently and robustly.
This paper addresses the problem of learning a task from demonstration. We adopt the framework of inverse reinforcement learning, where tasks are represented in the form of a reward function. Our contribution is a novel active learning algorithm that enables the learning agent to query the expert for more informative d…