Using Ensemble Techniques and Multi-Objectivization to Solve Reinforcement Learning Problems
نویسندگان
چکیده
Recent work on multi-objectivization has shown how a single-objective reinforcement learning problem can be turned into a multi-objective problem with correlated objectives, by providing multiple reward shaping functions. The information contained in these correlated objectives can be exploited to solve the base, singleobjective problem faster and better, given techniques specifically aimed at handling such correlated objectives. In this paper, we identify ensemble techniques as a set of methods that is suitable to solve multi-objectivized reinforcement learning problems. We empirically demonstrate their use on the Pursuit domain.
منابع مشابه
Multi-objectivization and ensembles of shapings in reinforcement learning
Ensemble techniques are a powerful approach to creating better decision makers in machine learning. Multiple decision makers are trained to solve a given task, grouped in an ensemble, and their decisions are aggregated. The ensemble derives its power from the diversity of its components, as the assumption is that they make mistakes on different inputs, and that the majority is more likely to be...
متن کاملReinforcement Learning on Multiple Correlated Signals
This extended abstract provides a brief overview of my PhD research on multi-objectivization and ensemble techniques in reinforcement learning.
متن کاملMulti-Objectivization in Reinforcement Learning
Multi-objectivization is the process of transforming a single objective problem into a multi-objective problem. Research in evolutionary optimization has demonstrated that the addition of objectives that are correlated with the original objective can make the resulting problem easier to solve compared to the original single-objective problem. In this paper we investigate the multi-objectivizati...
متن کاملMulticast Routing in Wireless Sensor Networks: A Distributed Reinforcement Learning Approach
Wireless Sensor Networks (WSNs) are consist of independent distributed sensors with storing, processing, sensing and communication capabilities to monitor physical or environmental conditions. There are number of challenges in WSNs because of limitation of battery power, communications, computation and storage space. In the recent years, computational intelligence approaches such as evolutionar...
متن کاملMachine learning algorithms in air quality modeling
Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...
متن کامل