scholarly journals Cooperative Multi-Robot Task Allocation with Reinforcement Learning

2021 ◽  
Vol 12 (1) ◽  
pp. 272
Author(s):  
Bumjin Park ◽  
Cheongwoong Kang ◽  
Jaesik Choi
Keyword(s):  
Reinforcement Learning ◽  
Task Allocation ◽  
Decision Process ◽  
Large Scale ◽  
Heuristic Methods ◽  
Learning Method ◽  
Multiple Robots ◽  
Markov Decision ◽  
Robot Task ◽  
Multi Robot

This paper deals with the concept of multi-robot task allocation, referring to the assignment of multiple robots to tasks such that an objective function is maximized. The performance of existing meta-heuristic methods worsens as the number of robots or tasks increases. To tackle this problem, a novel Markov decision process formulation for multi-robot task allocation is presented for reinforcement learning. The proposed formulation sequentially allocates robots to tasks to minimize the total time taken to complete them. Additionally, we propose a deep reinforcement learning method to find the best allocation schedule for each problem. Our method adopts the cross-attention mechanism to compute the preference of robots to tasks. The experimental results show that the proposed method finds better solutions than meta-heuristic methods, especially when solving large-scale allocation problems.

A Multi-Step Reinforcement Learning Algorithm

2010 ◽  
Vol 44-47 ◽  
pp. 3611-3615 ◽  
Author(s):  
Zhi Cong Zhang ◽  
Kai Shun Hu ◽  
Hui Yu Huang ◽  
Shuai Li ◽  
Shao Yong Zhao
Keyword(s):  
Reinforcement Learning ◽  
Markov Decision Process ◽  
Decision Process ◽  
Large Scale ◽  
Learning Algorithm ◽  
Machine Learning Method ◽  
Learning Method ◽  
K Value ◽  
Markov Decision ◽  
Action Value

Reinforcement learning (RL) is a state or action value based machine learning method which approximately solves large-scale Markov Decision Process (MDP) or Semi-Markov Decision Process (SMDP). A multi-step RL algorithm called Sarsa(,k) is proposed, which is a compromised variation of Sarsa and Sarsa(). It is equivalent to Sarsa if k is 1 and is equivalent to Sarsa() if k is infinite. Sarsa(,k) adjust its performance by setting k value. Two forms of Sarsa(,k), forward view Sarsa(,k) and backward view Sarsa(,k), are constructed and proved equivalent in off-line updating.

scholarly journals DQN as an alternative to Market-based approaches for Multi-Robot processing Task Allocation (MRpTA)

2021 ◽  
Vol 3 (1) ◽  
pp. 69-98
Author(s):  
Paul Gautier ◽  
Johann Laurent
Keyword(s):  
Reinforcement Learning ◽  
Task Allocation ◽  
Computing System ◽  
Processing Load ◽  
Robot System ◽  
Research Challenges ◽  
Local Solutions ◽  
Overall Efficiency ◽  
Robot Task ◽  
Multi Robot

Multi-robot task allocation (MRTA) problems require that robots make complex choices based on their understanding of a dynamic and uncertain environment. As a distributed computing system, the Multi-Robot System (MRS) must handle and distribute processing tasks (MRpTA). Each robot must contribute to the overall efficiency of the system based solely on a limited knowledge of its environment. Market-based methods are a natural candidate to deal processing tasks over a MRS but recent and numerous developments in reinforcement learning and especially Deep Q-Networks (DQN) provide new opportunities to solve the problem. In this paper we propose a new DQN-based method so that robots can learn directly from experience, and compare it with Market-based approaches as well with centralized and purely local solutions. Our study shows the relevancy of learning-based methods and also highlight research challenges to solve the processing load-balancing problem in MRS.

FLOW SHOP SCHEDULING WITH REINFORCEMENT LEARNING

2013 ◽  
Vol 30 (05) ◽  
pp. 1350014 ◽  
Author(s):  
ZHICONG ZHANG ◽  
WEIPING WANG ◽  
SHOUYAN ZHONG ◽  
KAISHUN HU
Keyword(s):  
Reinforcement Learning ◽  
Markov Decision Process ◽  
Decision Process ◽  
Large Scale ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Reward Function ◽  
Markov Decision

Reinforcement learning (RL) is a state or action value based machine learning method which solves large-scale multi-stage decision problems such as Markov Decision Process (MDP) and Semi-Markov Decision Process (SMDP) problems. We minimize the makespan of flow shop scheduling problems with an RL algorithm. We convert flow shop scheduling problems into SMDPs by constructing elaborate state features, actions and the reward function. Minimizing the accumulated reward is equivalent to minimizing the schedule objective function. We apply on-line TD(λ) algorithm with linear gradient-descent function approximation to solve the SMDPs. To examine the performance of the proposed RL algorithm, computational experiments are conducted on benchmarking problems in comparison with other scheduling methods. The experimental results support the efficiency of the proposed algorithm and illustrate that the RL approach is a promising computational approach for flow shop scheduling problems worthy of further investigation.

A reinforcement learning method based on an immune network adapted to a semi-Markov decision process

2009 ◽  
Vol 13 (2) ◽  
pp. 538-542 ◽  
Author(s):  
Nagahisa Kogawa ◽  
Masanao Obayashi ◽  
Kunikazu Kobayashi ◽  
Takashi Kuremoto
Keyword(s):  
Reinforcement Learning ◽  
Markov Decision Process ◽  
Decision Process ◽  
Immune Network ◽  
Learning Method ◽  
Markov Decision
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