scholarly journals Multi-Objective Reinforcement Learning for Designing Ethical Environments

2021 ◽  
Author(s):  
Manel Rodriguez-Soto ◽  
Maite Lopez-Sanchez ◽  
Juan A. Rodriguez Aguilar
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Agents ◽  
Moral Values ◽  
Design Environments ◽  
Multi Objective ◽  
Learning Techniques ◽  
Formal Framework ◽  
Theoretical Results

AI research is being challenged with ensuring that autonomous agents learn to behave ethically, namely in alignment with moral values. A common approach, founded on the exploitation of Reinforcement Learning techniques, is to design environments that incentivise agents to behave ethically. However, to the best of our knowledge, current approaches do not theoretically guarantee that an agent will learn to behave ethically. Here, we make headway along this direction by proposing a novel way of designing environments wherein it is formally guaranteed that an agent learns to behave ethically while pursuing its individual objectives. Our theoretical results develop within the formal framework of Multi-Objective Reinforcement Learning to ease the handling of an agent's individual and ethical objectives. As a further contribution, we leverage on our theoretical results to introduce an algorithm that automates the design of ethical environments.

scholarly journals An Overview of Inverse Reinforcement Learning Techniques

2021 ◽  
Author(s):  
Syed Ihtesham Hussain Shah ◽  
Giuseppe De Pietro
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Decision Process ◽  
Autonomous Agents ◽  
Theoretical Background ◽  
Inverse Reinforcement Learning ◽  
Reward Function ◽  
Learning Techniques ◽  
Markov Decision ◽  
Potential Use

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.

scholarly journals Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Jacopo Castellini ◽  
Frans A. Oliehoek ◽  
Rahul Savani ◽  
Shimon Whiteson
Keyword(s):  
Reinforcement Learning ◽  
Multiagent Systems ◽  
Autonomous Agents ◽  
Coordination Mechanism ◽  
Network Architectures ◽  
Value Functions ◽  
Multi Agent Systems ◽  
Learning Techniques ◽  
Coordination Requirements ◽  
Multi Agent

AbstractRecent years have seen the application of deep reinforcement learning techniques to cooperative multi-agent systems, with great empirical success. However, given the lack of theoretical insight, it remains unclear what the employed neural networks are learning, or how we should enhance their learning power to address the problems on which they fail. In this work, we empirically investigate the learning power of various network architectures on a series of one-shot games. Despite their simplicity, these games capture many of the crucial problems that arise in the multi-agent setting, such as an exponential number of joint actions or the lack of an explicit coordination mechanism. Our results extend those in Castellini et al. (Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS’19.International Foundation for Autonomous Agents and Multiagent Systems, pp 1862–1864, 2019) and quantify how well various approaches can represent the requisite value functions, and help us identify the reasons that can impede good performance, like sparsity of the values or too tight coordination requirements.

scholarly journals Optimal Policies for Quantum Markov Decision Processes

2021 ◽  
Author(s):  
Ming-Sheng Ying ◽  
Yuan Feng ◽  
Sheng-Gang Ying
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Quantum Systems ◽  
Sequential Decision Making ◽  
Mathematical Framework ◽  
Sequential Decision ◽  
Learning Techniques ◽  
Optimal Policies ◽  
Markov Decision ◽  
Programming Algorithms

AbstractMarkov decision process (MDP) offers a general framework for modelling sequential decision making where outcomes are random. In particular, it serves as a mathematical framework for reinforcement learning. This paper introduces an extension of MDP, namely quantum MDP (qMDP), that can serve as a mathematical model of decision making about quantum systems. We develop dynamic programming algorithms for policy evaluation and finding optimal policies for qMDPs in the case of finite-horizon. The results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.

scholarly journals Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning

2021 ◽  
pp. 1-1
Author(s):  
Stefan Schneider ◽  
Ramin Khalili ◽  
Adnan Manzoor ◽  
Haydar Qarawlus ◽  
Rafael Schellenberg ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Service Coordination ◽  
Multi Objective ◽  
Self Learning

scholarly journals On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic

2020 ◽  
Author(s):  
Bryan P Bednarski ◽  
Akash Deep Singh ◽  
William M Jones
Keyword(s):  
Public Health ◽  
Reinforcement Learning ◽  
Medical Equipment ◽  
Census Bureau ◽  
Learning Models ◽  
Public Health Emergencies ◽  
Medical Supplies ◽  
Learning Techniques ◽  
Disease Impact ◽  
Random States

Abstract objective This work investigates how reinforcement learning and deep learning models can facilitate the near-optimal redistribution of medical equipment in order to bolster public health responses to future crises similar to the COVID-19 pandemic. materials and methods The system presented is simulated with disease impact statistics from the Institute of Health Metrics (IHME), Center for Disease Control, and Census Bureau[1, 2, 3]. We present a robust pipeline for data preprocessing, future demand inference, and a redistribution algorithm that can be adopted across broad scales and applications. results The reinforcement learning redistribution algorithm demonstrates performance optimality ranging from 93-95%. Performance improves consistently with the number of random states participating in exchange, demonstrating average shortage reductions of 78.74% (± 30.8) in simulations with 5 states to 93.50% (± 0.003) with 50 states. conclusion These findings bolster confidence that reinforcement learning techniques can reliably guide resource allocation for future public health emergencies.

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