Customisable assistive plans as dynamic composition of services with normed-QoS

Reinforcement Learning (RL) methods provide a solution for decision-making problems under uncertainty. An agent finds a suitable policy through a reward function by interacting with a dynamic environment. However, for complex and large problems it is very difficult to specify and tune the reward function. Inverse Reinforcement Learning (IRL) may mitigate this problem by learning the reward function through expert demonstrations. This work exploits an IRL method named Max-Margin Algorithm (MMA) to learn the reward function for a robotic navigation problem. The learned reward function reveals the demonstrated policy (expert policy) better than all other policies. Results show that this method has better convergence and learned reward functions through the adopted method represents expert behavior more efficiently.

Section: Related Workmentioning

confidence: 99%

Inverse Reinforcement Learning Through Max-Margin Algorithm

Shah

Coronato

2021

“…An overview of the literature on the use of IoT in HMS is presented in [2] and recent developments and trends of HMS in terms of security issues, wireless communications, frameworks and health parameters are discussed with advantages and limitations. Ambient assisted living techniques may improve the quality of life of elderly with cognitive and physical disabilities, by solving assistive tasks such as medication management [19]. Social humanoid robot [9] can help to monitor indoor environmental quality [37].…”

Section: Related Workmentioning

confidence: 99%

A Self-Learning Autonomous and Intelligent System for the Reduction of Medication Errors in Home Treatments

Donnici¹,

Coronato²,

Naeem³

2021

The treatment process at home after hospitalization may become challenging for elders and people having any physical or cognitive disability. Such patients can, nowadays, be supported by Autonomous and Intelligent Monitoring Systems (AIMSs) that may get new levels of functionalities thanks to technologies like Reinforcement Learning, Deep Learning and Internet of Things. We present an AIMS that can assist impaired patients in taking medicines in accordance with their treatment plans. The demonstration of the AIMS via mobile app shows promising results and can improve the quality of healthcare at home.

“…ML is an application of AI that focuses on learning and improving itself from experience and without being explicitly programmed. ML emphasizes on developing algorithms that can access data and use it for self-learning [1,2,3] in an intelligent environments [4,5,6]. We are dealing with a certain number of sensors, which enable the IE [7] to be aware of the user's current action and goal.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Inverse Reinforcement Learning Techniques

Shah

Pietro

2021

In decision-making problems reward function plays an important role in finding the best policy. Reinforcement Learning (RL) provides a solution for decision-making problems under uncertainty in an Intelligent Environment (IE). However, it is difficult to specify the reward function for RL agents in large and complex problems. To counter these problems an extension of RL problem named Inverse Reinforcement Learning (IRL) is introduced, where reward function is learned from expert demonstrations. IRL is appealing for its potential use to build autonomous agents, capable of modeling others, deprived of compromising in performance of the task. This approach of learning by demonstrations relies on the framework of Markov Decision Process (MDP). This article elaborates original IRL algorithms along with their close variants to mitigate challenges. The purpose of this paper is to highlight an overview and theoretical background of IRL in the field of Machine Learning (ML) and Artificial Intelligence (AI). We presented a brief comparison between different variants of IRL in this article.