Abstract
An effective method to use centralized Q-learning in multi-robot task allocation
Highlights
With the rapid growth of technology, multi-robot systems (MRS) become most popular especially for complex applications
To emphasize the impact and successful results of the proposed approach, subG-CQL, algorithm, it is compared with the three other methods given in literature, centralized Q-learning, semi-centralized Q-learning and decentralized Q-learning
Q-learning method provides optimal solution for robotic applications, but it is problematic to use in multi-robot domains
Summary
With the rapid growth of technology, multi-robot systems (MRS) become most popular especially for complex applications. Auction-based task allocation approaches have advantages of implementation simplicity and distributed planning centralized decision-making ability [7]. As an example of learning-based coordination, robots use their past task allocation experiences for bidding future tasks [16]. One way to use QL in MRS is distributed learning approach in which the robots learn only for their own state-action pairs. The other way is centralized learning that works on joint state and joint action spaces Learning process takes place based on the feedback which is the measure of the changes in environment states as a result of agent’s action. In QL proposed by [22], an agent learns Q values of each state-action pair by using reward received as a feedback of its actions’ effect on environment states. Theoretical details of QL for both single-agent and multiagent cases are given below
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