A Machine Learning Approach to Reduce Uncertainty in Ex-ante LCA

Purpose: This paper explores the potential of machine learning (ML) algorithms to mitigate uncertainty in early environmental assessments (ex-ante LCA), which are hindered by prospective nature and limited quantitative data availability. Methods: A systematic literature review with keyword searches on Scopus identified three ML categorization...

Investigating the extent to which inverse reinforcement learning can learn Rrewards from noisy demonstrations

Inverse Reinforcement Learning (IRL) aims to recover a reward function from expert demonstrations in a Markov Decision Process (MDP). The objective is to understand the underlying intentions and behaviors of experts and derive a reward function based on their reasoning, rather than their exact actions. However, expert demonstrations can be...

Inverse Reinforcement Learning (IRL) in Presence of Risk and Uncertainty Related Cognitive Biases: To what extent can IRL learn rewards from expert demonstrations with loss and risk aversion?

A key issue in Reinforcement Learning (RL) research is the difficulty of defining rewards. Inverse Reinforcement Learning (IRL) is a technique that addresses this challenge by learning the rewards from expert demonstrations. In a realistic setting, expert demonstrations are collected from humans, and it is important to acknowledge that these...

What are the implications of Curriculum Learning strategy on IRL methods?: Investigating Inverse Reinforcement Learning from Human Behavior

Inverse Reinforcement Learning (IRL) is a subfield of Reinforcement Learning (RL) that focuses on recovering the reward function using expert demonstrations. In the field of IRL, Adversarial IRL (AIRL) is a promising algorithm that is postulated to recover non-linear rewards in environments with unknown dynamics. This study investigates the...

Conflicting demonstrations in Inverse Reinforcement Learning

This paper aims to investigate the effect of conflicting demonstrations on Inverse Reinforcement Learning (IRL). IRL is a method to understand the intent of an expert, by only feeding it demonstrations of that expert, which may be a promising approach for areas such as self driving vehicles, where there are a lot of demonstrations from experts....

Domain-focused dataset discovery for tabular datasets, using easily-available information about the domain

Dataset discovery techniques originally required datasets to have the same domain which made them unsuitable to be used on a larger scale. To avoid this requirement, newer techniques use additional information, aside from the datasets being processed, to better understand the data. They might rely on a knowledge base that describes the meaning...