scholarly journals A Deep Reinforcement Learning Approach to The Ancient Indian Game - Chowka Bhara

2021 ◽  
Author(s):  
Annapurna P Patil ◽  
SANJAY RAGHAVENDRA ◽  
Shruthi Srinarasi ◽  
Reshma Ram
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Experimental Results ◽  
Learning Approach ◽  
Board Game ◽  
Q Learning

<p>Reinforcement Learning (RL) is the study of how Artificial Intelligence (AI) agents learn to make their own decisions in an environment to maximize the cumulative reward received. Although there has been notable progress in the application of RL for games, the category of ancient Indian games has remained almost untouched. Chowka Bhara is one such ancient Indian board game. This work aims at developing a Q-Learning-based RL Chowka Bhara player whose strategies and methodologies are obtained from three Strategic Players viz. Fast Player, Random Player, and Balanced Player. It is observed through the experimental results that the Q-Learning Player outperforms all three Strategic Players.</p>

scholarly journals A Deep Reinforcement Learning Approach to The Ancient Indian Game - Chowka Bhara

2021 ◽  
Author(s):  
Annapurna P Patil ◽  
SANJAY RAGHAVENDRA ◽  
Shruthi Srinarasi ◽  
Reshma Ram
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Experimental Results ◽  
Learning Approach ◽  
Board Game ◽  
Q Learning

<p>Reinforcement Learning (RL) is the study of how Artificial Intelligence (AI) agents learn to make their own decisions in an environment to maximize the cumulative reward received. Although there has been notable progress in the application of RL for games, the category of ancient Indian games has remained almost untouched. Chowka Bhara is one such ancient Indian board game. This work aims at developing a Q-Learning-based RL Chowka Bhara player whose strategies and methodologies are obtained from three Strategic Players viz. Fast Player, Random Player, and Balanced Player. It is observed through the experimental results that the Q-Learning Player outperforms all three Strategic Players.</p>

scholarly journals Averaged Soft Actor-Critic for Deep Reinforcement Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Feng Ding ◽  
Guanfeng Ma ◽  
Zhikui Chen ◽  
Jing Gao ◽  
Peng Li
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Large Scale ◽  
Experimental Results ◽  
High Dimensional ◽  
Training Process ◽  
Value Network ◽  
Q Learning ◽  
Important Impact ◽  
The Stability

With the advent of the era of artificial intelligence, deep reinforcement learning (DRL) has achieved unprecedented success in high-dimensional and large-scale artificial intelligence tasks. However, the insecurity and instability of the DRL algorithm have an important impact on its performance. The Soft Actor-Critic (SAC) algorithm uses advanced functions to update the policy and value network to alleviate some of these problems. However, SAC still has some problems. In order to reduce the error caused by the overestimation of SAC, we propose a new SAC algorithm called Averaged-SAC. By averaging the previously learned action-state estimates, it reduces the overestimation problem of soft Q-learning, thereby contributing to a more stable training process and improving performance. We evaluate the performance of Averaged-SAC through some games in the MuJoCo environment. The experimental results show that the Averaged-SAC algorithm effectively improves the performance of the SAC algorithm and the stability of the training process.

Reinforcement Learning for Cloud Computing Digital Library

2014 ◽  
Vol 571-572 ◽  
pp. 105-108
Author(s):  
Lin Xu
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Reinforcement Learning ◽  
Digital Library ◽  
Learning Algorithms ◽  
Experimental Results ◽  
Current Status ◽  
Self Learning ◽  
New Framework

This paper proposes a new framework of combining reinforcement learning with cloud computing digital library. Unified self-learning algorithms, which includes reinforcement learning, artificial intelligence and etc, have led to many essential advances. Given the current status of highly-available models, analysts urgently desire the deployment of write-ahead logging. In this paper we examine how DNS can be applied to the investigation of superblocks, and introduce the reinforcement learning to improve the quality of current cloud computing digital library. The experimental results show that the method works more efficiency.

Cloud Load Balancing and Reinforcement Learning

2018 ◽  
pp. 266-291
Author(s):  
Abdelghafour Harraz ◽  
Mostapha Zbakh
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Load Balancing ◽  
Decision Process ◽  
Cloud System ◽  
Human Intervention ◽  
Q Learning ◽  
State Action ◽  
Learning Techniques ◽  
Markov Decision

Artificial Intelligence allows to create engines that are able to explore, learn environments and therefore create policies that permit to control them in real time with no human intervention. It can be applied, through its Reinforcement Learning techniques component, using frameworks such as temporal differences, State-Action-Reward-State-Action (SARSA), Q Learning to name a few, to systems that are be perceived as a Markov Decision Process, this opens door in front of applying Reinforcement Learning to Cloud Load Balancing to be able to dispatch load dynamically to a given Cloud System. The authors will describe different techniques that can used to implement a Reinforcement Learning based engine in a cloud system.

CATEGORIZATION OF CONTINUOUS NUMERIC PERCEPTS BY MODIFIED FUZZY ART WITH Q-LEARNING

2006 ◽  
Vol 20 (02) ◽  
pp. 113-127 ◽  
Author(s):  
HIROAKI UEDA ◽  
HIDEAKI KIMOTO ◽  
TAKESHI NARAKI ◽  
KENICHI TAKAHASHI ◽  
TETSUHIRO MIYAHARA
Keyword(s):  
Reinforcement Learning ◽  
Experimental Results ◽  
New Method ◽  
Agent Behavior ◽  
Q Learning ◽  
Fuzzy Art ◽  
Efficient Learning

We propose a new method to categorize continuous numeric percepts for Q-learning, where percept vectors are classified into categories on the basis of fuzzy ART and Q-learning uses categories as states to acquire rules for agent behavior. For efficient learning, we modify fuzzy ART to reduce the number of categories without deteriorating the efficiency of reinforcement learning. In our modification, a vigilance parameter is defined for each category in order to control the size of a category and it is updated during learning. The method to update a vigilance parameter is based on category integration, which contributes to reducing the number of categories. Here, we define the similarity for any category pair to judge whether category integration should be performed or not. When two categories are integrated into a new category, a vigilance parameter for the category is calculated and categories used for integration are discarded, so that the number of categories is reduced without restricting the number of categories. Experimental results show that Q-learning with modified fuzzy ART acquires good rules for agent behavior more efficiently than Q-learning with ordinary fuzzy ART, although the number of categories generated by modified fuzzy ART is much less than that generated by ordinary one.

scholarly journals Reinforcement-Learning Approach Guidelines for Energy Management

2019 ◽  
Vol 15 (3) ◽  
pp. 283-293 ◽  
Author(s):  
Yohann Rioual ◽  
Johann Laurent ◽  
Jean-Philippe Diguet
Keyword(s):  
Reinforcement Learning ◽  
Energy Management ◽  
Sensor Node ◽  
Learning Algorithm ◽  
Autonomous Systems ◽  
Difficult Problem ◽  
Learning Approach ◽  
Neural Network Approach ◽  
Q Learning ◽  
Energy Harvesting Devices

IoT and autonomous systems are in charge of an increasing number of sensing, processing and communications tasks. These systems may be equipped with energy harvesting devices. Nevertheless, the energy harvested is uncertain and variable, which makes it difficult to manage the energy in these systems. Reinforcement learning algorithms can handle such uncertainties, however selecting the adapted algorithm is a difficult problem. Many algorithms are available and each has its own advantages and drawbacks. In this paper, we try to provide an overview of different approaches to help designer to determine the most appropriate algorithm according to its application and system. We focus on Q-learning, a popular reinforcement learning algorithm and several of these variants. The approach of Q-learning is based on the use of look up table, however some algorithms use a neural network approach. We compare different variants of Q-learning for the energy management of a sensor node. We show that depending on the desired performance and the constraints inherent in the application of the node, the appropriate approach changes.

scholarly journals Choice of cargo delivery option in multimodal connection based on reinforcement learning

2021 ◽  
Vol 2131 (3) ◽  
pp. 032103
Author(s):  
A P Badetskii ◽  
O A Medved
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Digital Technologies ◽  
Qualitative Assessment ◽  
Q Learning ◽  
Cargo Delivery ◽  
Route Option

Abstract The article discusses the issues of choosing a route and an option of cargo flows in multimodal connection in modern conditions. Taking into account active development of artificial intelligence and digital technologies in all types of production activities, it is proposed to use reinforcement learning algorithms to solve the problem. An analysis of the existing algorithms has been carried out, on the basis of which it was found that when choosing a route option for cargo in a multimodal connection, it would be useful to have a qualitative assessment of terminal states. To obtain such an estimate, the Q-learning algorithm was applied in the article, which showed sufficient convergence and efficiency.

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