scholarly journals Multi-Agent Systems of Inverse Reinforcement Learners in Complex Games

2017 ◽  
Author(s):  
Dave Mobley
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Computer Games ◽  
Computer Game ◽  
Role Playing ◽  
Multi Agent Systems ◽  
Role Playing Games ◽  
Starting Point ◽  
Multi Agent ◽  
Real World Problems

Real-world problems exhibit a few defining criteria that make them hard for computers to solve. Problems such as driving a car or flying a helicopter have primary goals of reaching a destination as well as doing it safely and timely. These problems must each manage many resources and tasks to achieve their primary goals. The tasks themselves are made up of states that are represented by variables or features. As the feature set grows, the problems become intractable. Computer games are smaller problems but also are representative of real-world problems of this type. In my research, I will look at a particular class of computer game, namely computer role-playing games (RPGs), which are made up of a collection of overarching goals such as improving the player avatar, navigating a virtual world, and keeping the avatar alive. While playing there are also subtasks such as combatting other characters and managing inventory which are not primary, but yet important to overall game play. I will be exploring tiered Reinforcement Learning techniques coupled with training from expert policies using Inverse Reinforcement Learning as a starting point on learning how to play a complex game while attempting to extrapolate ideal goals and rewards.

Ayurveda for agents: an attempt to bring the sciences of natural and artificial intelligence closer together

2011 ◽  
Vol 26 (1) ◽  
pp. 31-33 ◽  
Author(s):  
Rogier M. van Eijk
Keyword(s):  
Artificial Intelligence ◽  
Practical Reasoning ◽  
Well Being ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
New Science ◽  
Starting Point ◽  
Multi Agent ◽  
Health And Well Being ◽  
Real World Problems

AbstractThis paper advocates a new science of intelligence, one that is holistic, multi-disciplinary, oriented to crucial values as health and well-being and able to contribute to the solution of real-world problems. As a starting point we study the interplay between two research disciplines that until now have been hardly related to each other: Ayurveda and multi-agent systems. We consider some possible results of the cross fertilisation like for instance the application of ayurvedic knowledge to improve the skills of practical reasoning agents.

An applied multi-agent system within the framework of a player-centered probabilistic computer game

2018 ◽  
Vol 09 (01) ◽  
pp. 1750063
Author(s):  
L. S. Kuravsky ◽  
S. I. Popkov ◽  
S. L. Artemenkov
Keyword(s):  
Computer Games ◽  
Probabilistic Model ◽  
Cognitive Abilities ◽  
Computer Game ◽  
Model Parameters ◽  
Multi Agent System ◽  
Objective Functions ◽  
Multi Agent Systems ◽  
Agent System ◽  
Multi Agent

The probabilistic model to represent the behavior of an applied multi-agent system that introduces the interaction between a set of agents and Player has been developed within the framework of player-centered probabilistic computer games. The approach features are given with the aid of a game developed for testing cognitive abilities. The agent’s behavior is nondeterministic and therefore unpredictable from Player viewpoint. The system allows both coordinated and autonomous agent’s behavior that depends on availability of information about the presence and position of workable agents for each other. Agent’s behavior is determined with the aid of the algorithm that includes identification of the probabilistic model parameters using maximized objective functions representing individual and group probabilities for Player defeating. Both the model and algorithm ensure the behavior control for relevant applied multi-agent systems.

scholarly journals Reinforcement Learning Approaches in Social Robotics

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1292
Author(s):  
Neziha Akalin ◽  
Amy Loutfi
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Social Robotics ◽  
Research Field ◽  
Social Robots ◽  
Learning Approaches ◽  
Reward Function ◽  
Optimal Behavior ◽  
Learning Challenges ◽  
Starting Point

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field.

Improvement on Supporting Machine Learning Algorithm for Solving Problem in Immediate Decision Making

2012 ◽  
Vol 566 ◽  
pp. 572-579
Author(s):  
Abdolkarim Niazi ◽  
Norizah Redzuan ◽  
Raja Ishak Raja Hamzah ◽  
Sara Esfandiari
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Multi Agent Systems ◽  
Combined Model ◽  
Q Learning ◽  
Agent Systems ◽  
Multi Agent ◽  
Case Base ◽  
Case Base Reasoning ◽  
Robotic Tool

In this paper, a new algorithm based on case base reasoning and reinforcement learning (RL) is proposed to increase the convergence rate of the reinforcement learning algorithms. RL algorithms are very useful for solving wide variety decision problems when their models are not available and they must make decision correctly in every state of system, such as multi agent systems, artificial control systems, robotic, tool condition monitoring and etc. In the propose method, we investigate how making improved action selection in reinforcement learning (RL) algorithm. In the proposed method, the new combined model using case base reasoning systems and a new optimized function is proposed to select the action, which led to an increase in algorithms based on Q-learning. The algorithm mentioned was used for solving the problem of cooperative Markov’s games as one of the models of Markov based multi-agent systems. The results of experiments Indicated that the proposed algorithms perform better than the existing algorithms in terms of speed and accuracy of reaching the optimal policy.

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