DeepAI AI Chat
Log In Sign Up

A Practical Guide to Multi-Objective Reinforcement Learning and Planning

03/17/2021
by   Conor F. Hayes, et al.
55

Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems.

READ FULL TEXT
02/04/2014

A Survey of Multi-Objective Sequential Decision-Making

Sequential decision-making problems with multiple objectives arise natur...
02/08/2023

A Scale-Independent Multi-Objective Reinforcement Learning with Convergence Analysis

Many sequential decision-making problems need optimization of different ...
03/02/2023

Reinforcement Learning Guided Multi-Objective Exam Paper Generation

To reduce the repetitive and complex work of instructors, exam paper gen...
04/01/2022

Automating Staged Rollout with Reinforcement Learning

Staged rollout is a strategy of incrementally releasing software updates...
09/09/2020

Multi-Objective Reinforcement Learning for Infectious Disease Control with Application to COVID-19 Spread

Severe infectious diseases such as the novel coronavirus (COVID-19) pose...
09/10/2020

Multi-Objective Parameter-less Population Pyramid for Solving Industrial Process Planning Problems

Evolutionary methods are effective tools for obtaining high-quality resu...
08/17/2021

Monolithic vs. hybrid controller for multi-objective Sim-to-Real learning

Simulation to real (Sim-to-Real) is an attractive approach to construct ...