Results 81 to 90 of about 311 (117)

Towards Generalized Inverse Reinforcement Learning

CoRR
This paper studies generalized inverse reinforcement learning (GIRL) in Markov decision processes (MDPs), that is, the problem of learning the basic components of an MDP given observed behavior (policy) that might not be optimal. These components include not only the reward function and transition probability matrices, but also the action space and ...
Chaosheng Dong, Yijia Wang
openaire   +2 more sources

Maximum entropy inverse reinforcement learning for campus spatial optimization from Wi-Fi probe trajectories: a case study of Southeast University Wuxi Campus

Architectural Intelligence
With the outward expansion of university campuses toward suburban areas, the modern campus has evolved into an increasingly independent social space, providing an organizational setting in which users’ daily activities rely on diverse on-campus ...
Guangjin Wang, Ruike Huang, Xiang Li, Li Li   +3 more
doaj   +1 more source

AIMATDESIGN: knowledge-augmented reinforcement learning for inverse materials design under data scarcity

npj Computational Materials
With the growing demand for novel materials, machine learning-driven inverse design methods face significant challenges in reconciling the high-dimensional materials composition space with limited experimental data.
Yeyong Yu, Xilei Bian, Jie Xiong, Xing Wu, Quan Qian   +4 more
doaj   +1 more source

Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence

IEEE Access
This article presents a literature review of the past five years of studies using Deep Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic manipulation tasks.
Recep Ozalp, Aysegul Ucar, Cuneyt Guzelis   +2 more
doaj   +1 more source

Compatible Reward Inverse Reinforcement Learning.

, 2017
Inverse Reinforcement Learning (IRL) is an effective approach to recover a reward function that explains the behavior of an expert by observing a set of demonstrations. This paper is about a novel model-free IRL approach that, differently from most of the existing IRL algorithms, does not require to specify a function space where to search for the ...
METELLI, ALBERTO MARIA, M. Pirotta, M. Restelli   +2 more
openaire   +3 more sources

A survey of inverse reinforcement learning [PDF]

Artificial Intelligence Review, 2022
AbstractLearning from demonstration, or imitation learning, is the process of learning to act in an environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a specific form of learning from demonstration that attempts to estimate the reward function of a Markov decision process from examples provided by the teacher.
Stephen Adams, Peter Beling, Tyler Cody   +2 more
exaly   +3 more sources

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Inverse reinforcement learning with evaluation

Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., 2006
Reinforcement learning (RL) is a method that helps programming an autonomous agent through human-like objectives as reinforcements, where the agent is responsible for discovering the best actions to fulfil the objectives. Nevertheless, it is not easy to disentangle human objectives in reinforcement like objectives.
Valdinei Freire da Silva, Anna Helena Reali Costa, Pedro U. Lima   +2 more
openaire   +1 more source

Hierarchical Bayesian Inverse Reinforcement Learning

IEEE Transactions on Cybernetics, 2015
Inverse reinforcement learning (IRL) is the problem of inferring the underlying reward function from the expert's behavior data. The difficulty in IRL mainly arises in choosing the best reward function since there are typically an infinite number of reward functions that yield the given behavior data as optimal.
Choi, JD Choi, Jae-Deug, Kim, KE Kim, Kee-Eung   +1 more
openaire   +3 more sources

A survey of inverse reinforcement learning techniques

International Journal of Intelligent Computing and Cybernetics, 2012
Purpose – This purpose of this paper is to provide an overview of the theoretical background and applications of inverse reinforcement learning (IRL).Design/methodology/approach – Reinforcement learning (RL) techniques provide a powerful solution for sequential decision making problems under uncertainty. RL uses an agent equipped with a reward function
Shao, Zhifei, Er, Meng Joo
exaly   +4 more sources

Apprenticeship learning via inverse reinforcement learning

Twenty-first international conference on Machine learning - ICML '04, 2004
We consider learning in a Markov decision process where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we want to learn to perform. This setting is useful in applications (such as the task of driving) where it may be difficult to write down an explicit reward function specifying ...
Pieter Abbeel, Andrew Y. Ng
openaire   +1 more source