Combining a gradient-based method and an evolution strategy for multi-objective reinforcement learning

Chen, Diqi; Wang, Yizhou; Gao, Wen

doi:10.1007/s10489-020-01702-7

Cited by 23 publications

(20 citation statements)

References 38 publications

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“…e goal of the reinforcement learning algorithm is to find an optimal strategy based on the Markov decision process to maximize the expected cumulative return. In this section, the driving distance of the electric vehicle, the total driving and charging time, and the charging economy are optimized in parallel to provide the electric vehicle owner with the best electric vehicle charging navigation scheduling strategy [21,22].…”

Section: Electric Vehicle Charging Navigation Scheduling Strategy Based On Reinforcement Learningmentioning

confidence: 99%

Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling

Liu¹,

Chen

Zhu

2021

Mathematical Problems in Engineering

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The popularization of electric vehicles faces problems such as difficulty in charging, difficulty in selecting fast charging locations, and comprehensive consideration of multiple factors and vehicle interactions. With the increasingly mature application of navigation technology in vehicle-road coordination and other aspects, the proposal of an optimal dynamic charging method for electric fleets based on adaptive learning makes it possible for edge computing to process electric fleets to effectively execute the optimal route charging plan. We propose a method of electric vehicle charging service scheduling based on reinforcement learning. First, an intelligent transportation system is proposed, and on this basis a framework for the interaction between fast charging stations and electric vehicles is established. Subsequently, a dynamic travel time model for traffic sections was established. Based on the habits of electric vehicle owners, an electric vehicle charging navigation model and a reinforcement learning reward model were proposed. Finally, an electric vehicle charging navigation scheduling method is proposed to optimize the service resources of the fast charging stations in the area. The simulation results show that the method balances the charging load between stations, can effectively improve the charging efficiency of electric vehicles, and increases user satisfaction.

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Section: Electric Vehicle Charging Navigation Scheduling Strategy Based On Reinforcement Learningmentioning

confidence: 99%

Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling

Liu¹,

Chen

Zhu

2021

Mathematical Problems in Engineering

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“…Thanks to this ability to fine tune the loss function according to the environment and agent history, EPG can learn faster than a standard RL agent. Diqi Chen and Gao [182] proposed a hybrid agent to approximate the Pareto frontier uniformly in a multi-objective decision-making problem. The authors argued that despite the fast convergence of DRL, it cannot guarantee a uniformly approximated Pareto frontier.…”

Section: Hybrid Deep Reinforcement Learning and Evolution Strategies Algorithmsmentioning

confidence: 99%

“…On the other hand, ES achieve a well-distributed Pareto frontier, but they face difficulties optimizing a DNN. Therefore, Diqi Chen and Gao [182] proposed a two-stage multi-objective reinforcement learning (MORL) framework. In the first stage, a multi-policy soft actor-critic algorithm learns multiple policies collaboratively.…”

Section: Hybrid Deep Reinforcement Learning and Evolution Strategies Algorithmsmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹,

Saaybi²,

Rietbergen³

et al. 2021

Preprint

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<div>Deep Reinforcement Learning (DRL) has the potential to surpass human-level control in sequential decision-making problems. Evolution Strategies (ESs) have different characteristics than DRL, yet they are promoted as a scalable alternative. </div><div>To get insights into their strengths and weaknesses, in this paper, we put the two approaches side by side. After presenting the fundamental concepts and algorithms for each of the two approaches, they are compared from the perspectives of scalability, exploration, adaptation to dynamic environments, and multi-agent learning. Then, the paper discusses hybrid algorithms, combining aspects of both DRL and ESs, and how they attempt to capitalize on the benefits of both techniques. Lastly, both approaches are compared based on the set of applications they support, showing their potential for tackling real-world problems.</div><div>This paper aims to present an overview of how DRL and ESs can be used, either independently or in unison, to solve specific learning tasks. It is intended to guide researchers to select which method suits them best and provides a bird's eye view of the overall literature in the field. Further, we also provide application scenarios and open challenges. </div>

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“…Diqi Chen and Gao [172] proposed a hybrid agent to approximate the Pareto frontier uniformly in a multi-objective decision-making problem. The authors argued that despite the fast convergence of DRL, it cannot guarantee a uniformly approximated Pareto frontier.…”

Section: Hybrid Deep Reinforcement Learning and Evolution Strategies Algorithmsmentioning

confidence: 99%

“…On the other hand, ES achieve a well-distributed Pareto frontier, but they face difficulties optimizing a DNN. Therefore, Diqi Chen and Gao [172] proposed a two-stage multi-objective reinforcement learning (MORL) framework. In the first stage, a multi-policy soft actor-critic algorithm learns multiple policies collaboratively.…”

Section: Hybrid Deep Reinforcement Learning and Evolution Strategies Algorithmsmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹,

Saaybi²,

Rietbergen³

et al. 2021

Preprint

View full text Add to dashboard Cite

<div>Deep Reinforcement Learning (DRL) and Evolution Strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist.</div><div>To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. </div><div>After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects such as scalability, exploration, adaptation to dynamic environments, and multi-agent learning. </div><div>Then, the benefits of hybrid algorithms that combine concepts from DRL and ESs are highlighted. </div><div>Finally, to have an indication about how they compare in real-world applications, a survey of the literature for the set of applications they support is provided.</div>

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Combining a gradient-based method and an evolution strategy for multi-objective reinforcement learning

Cited by 23 publications

References 38 publications

Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling

Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

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