scholarly journals Multi-Agent Cooperation Based on Reinforcement Learning with Internal Reward in Maze Problem

2018 ◽  
Vol 11 (4) ◽  
pp. 321-330
Author(s):  
Fumito UWANO ◽  
Naoki TATEBE ◽  
Yusuke TAJIMA ◽  
Masaya NAKATA ◽  
Tim KOVACS ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Multi Agent ◽  
Agent Cooperation ◽  
Maze Problem

Comparison Between Reinforcement Learning Methods with Different Goal Selections in Multi-Agent Cooperation

2017 ◽  
Vol 21 (5) ◽  
pp. 917-929 ◽  
Author(s):  
Fumito Uwano ◽  
◽  
Keiki Takadama
Keyword(s):  
Reinforcement Learning ◽  
Learning Process ◽  
Cooperative Behavior ◽  
Learning Methods ◽  
Q Learning ◽  
Designed Experiments ◽  
Multi Agent ◽  
Agent Cooperation ◽  
Maze Problem

This study discusses important factors for zero communication, multi-agent cooperation by comparing different modified reinforcement learning methods. The two learning methods used for comparison were assigned different goal selections for multi-agent cooperation tasks. The first method is called Profit Minimizing Reinforcement Learning (PMRL); it forces agents to learn how to reach the farthest goal, and then the agent closest to the goal is directed to the goal. The second method is called Yielding Action Reinforcement Learning (YARL); it forces agents to learn through a Q-learning process, and if the agents have a conflict, the agent that is closest to the goal learns to reach the next closest goal. To compare the two methods, we designed experiments by adjusting the following maze factors: (1) the location of the start point and goal; (2) the number of agents; and (3) the size of maze. The intensive simulations performed on the maze problem for the agent cooperation task revealed that the two methods successfully enabled the agents to exhibit cooperative behavior, even if the size of the maze and the number of agents change. The PMRL mechanism always enables the agents to learn cooperative behavior, whereas the YARL mechanism makes the agents learn cooperative behavior over a small number of learning iterations. In zero communication, multi-agent cooperation, it is important that only agents that have a conflict cooperate with each other.

scholarly journals Research on Tensor-Based Cooperative and Competitive in Multi-Agent Reinforcement Learning

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Tsega Weldu Araya ◽  
Md Rashed Ibn Nawab ◽  
A. P. Yuan Ling
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Data Representation ◽  
Training Data ◽  
Two Dimensional ◽  
Multiple Agents ◽  
Learning Agents ◽  
Dimensional Array ◽  
Multi Agent ◽  
Agent Cooperation

As technology overgrows, the assortment of information and the density of work becomes demanding to manage. To resolve the density of employment and human labor, machine-learning (ML) technology developed. Reinforcement learning (RL) is the recent advancement of ML studies. Multi-agent reinforcement learning (MARL) is useful to train multiple agents in the surrounding environment. The previous research studies focused on two-agent cooperation. Their data representation was held in a two-dimensional array, which is called a matrix. The limitation of this two-dimensional array appears as the training data of agents increases. The growth in the training data of agents creates storage drawbacks and data redundancy. Our first aim in this research is to improve an algorithm that can represent MARL training in tensor. In MARL, multiple agents are work together to achieve joint work. To share the training records and data of numerous agents, we need to collect the previous cumulative experience of agents in tensor. Secondly, we will discover the agent's cooperation and competition, with local and global goals of agents in MARL. Local goals are the cooperation of agents in a group or team where we use the training model as a student and teacher agent. The global goal is the competition between two contrary teams to acquire the reward. All learning agents have their Q table for storing the individual agent's training data in an environment. The growth in the number of learning agents, their training experience in Q tables, and the requirement for representing multiple data become the most challenging issue. We introduce tensor to store various data to resolve the challenges for data representation in multiple agent associations. Tensor is expressed as the three-dimensional array, although it is an N-way array, which is useful for representing and accessing numerous data. Finally, we will implement an algorithm for learning three cooperative agents against the opposed team using a tensor-based framework in the Q learning algorithm. We will provide an algorithm that can store the training records and data of multiple agents. Tensor advances to get a small storage size than the matrix for the training records of agents. Although three agent cooperation benefits to having maximum optimal reward.

Joint Action Learning for Multi-Agent Cooperation using Recurrent Reinforcement Learning

Digitale Welt ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 79-84
Author(s):  
Jorrit E. Posor ◽  
Lenz Belzner ◽  
Alexander Knapp
Keyword(s):  
Reinforcement Learning ◽  
Joint Action ◽  
Action Learning ◽  
Multi Agent ◽  
Agent Cooperation

Multi-Agent Cognition Difference Reinforcement Learning for Multi-Agent Cooperation

2021 ◽  
Author(s):  
Huimu Wang ◽  
Tenghai Qiu ◽  
Zhen Liu ◽  
Zhiqiang Pu ◽  
Jianqiang Yi ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Multi Agent ◽  
Agent Cooperation
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