scholarly journals A Dimensional Comparison between Evolutionary Algorithm and Deep Reinforcement Learning Methodologies for Autonomous Surface Vehicles with Water Quality Sensors

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2862
Author(s):  
Samuel Yanes Luis ◽  
Daniel Gutiérrez-Reina ◽  
Sergio Toral Marín
Keyword(s):  
Water Quality ◽  
Reinforcement Learning ◽  
Evolutionary Algorithm ◽  
Stability And Convergence ◽  
Learning Approaches ◽  
Q Learning ◽  
Autonomous Surface Vehicles ◽  
High Space ◽  
The Stability ◽  
Water Quality Sensors

The monitoring of water resources using Autonomous Surface Vehicles with water-quality sensors has been a recent approach due to the advances in unmanned transportation technology. The Ypacaraí Lake, the biggest water resource in Paraguay, suffers from a major contamination problem because of cyanobacteria blooms. In order to supervise the blooms using these on-board sensor modules, a Non-Homogeneous Patrolling Problem (a NP-hard problem) must be solved in a feasible amount of time. A dimensionality study is addressed to compare the most common methodologies, Evolutionary Algorithm and Deep Reinforcement Learning, in different map scales and fleet sizes with changes in the environmental conditions. The results determined that Deep Q-Learning overcomes the evolutionary method in terms of sample-efficiency by 50–70% in higher resolutions. Furthermore, it reacts better than the Evolutionary Algorithm in high space-state actions. In contrast, the evolutionary approach shows a better efficiency in lower resolutions and needs fewer parameters to synthesize robust solutions. This study reveals that Deep Q-learning approaches exceed in efficiency for the Non-Homogeneous Patrolling Problem but with many hyper-parameters involved in the stability and convergence.

scholarly journals Reinforcement Learning vs Genetic Algorithms in Game-Theoretic Cyber-Security

2018 ◽  
Author(s):  
Stefan Niculae
Keyword(s):  
Reinforcement Learning ◽  
Cyber Security ◽  
Large Scale ◽  
Human Performance ◽  
Learning Approaches ◽  
Classifier Systems ◽  
Penetration Testing ◽  
Q Learning ◽  
Game Theoretic ◽  
Security Game

Penetration testing is the practice of performing a simulated attack on a computer system in order to reveal its vulnerabilities. The most common approach is to gain information and then plan and execute the attack manually, by a security expert. This manual method cannot meet the speed and frequency required for efficient, large-scale secu- rity solutions development. To address this, we formalize penetration testing as a security game between an attacker who tries to compro- mise a network and a defending adversary actively protecting it. We compare multiple algorithms for finding the attacker’s strategy, from fixed-strategy to Reinforcement Learning, namely Q-Learning (QL), Extended Classifier Systems (XCS) and Deep Q-Networks (DQN). The attacker’s strength is measured in terms of speed and stealthi- ness, in the specific environment used in our simulations. The results show that QL surpasses human performance, XCS yields worse than human performance but is more stable, and the slow convergence of DQN keeps it from achieving exceptional performance, in addition, we find that all of these Machine Learning approaches outperform fixed-strategy attackers.

scholarly journals Selective auditory attention detection using dynamic learning systems: The study of RNN and reinforcement learning

2021 ◽  
Author(s):  
Masoud Geravanchizadeh ◽  
Hossein Roushan
Keyword(s):  
Reinforcement Learning ◽  
Detection System ◽  
Auditory Attention ◽  
Final Decision ◽  
Learning Approaches ◽  
Cocktail Party ◽  
Dynamic Learning ◽  
Learning Stage ◽  
Q Learning ◽  
Markov Decision

AbstractThe cocktail party phenomenon describes the ability of the human brain to focus auditory attention on a particular stimulus while ignoring other acoustic events. Selective auditory attention detection (SAAD) is an important issue in the development of brain-computer interface systems and cocktail party processors. This paper proposes a new dynamic attention detection system to process the temporal evolution of the input signal. In the proposed dynamic system, after preprocessing of the input signals, the probabilistic state space of the system is formed. Then, in the learning stage, different dynamic learning methods, including recurrent neural network (RNN) and reinforcement learning (Markov decision process (MDP) and deep Q-learning) are applied to make the final decision as to the attended speech. Among different dynamic learning approaches, the evaluation results show that the deep Q-learning approach (MDP+RNN) provides the highest classification accuracy (94.2%) with the least detection delay. The proposed SAAD system is advantageous, in the sense that the detection of attention is performed dynamically for the sequential inputs. Also, the system has the potential to be used in scenarios, where the attention of the listener might be switched in time in the presence of various acoustic events.

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.

Network Parameter Setting for Reinforcement Learning Approaches Using Neural Networks

2011 ◽  
Vol 15 (7) ◽  
pp. 822-930 ◽  
Author(s):  
Kazuaki Yamada ◽  
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Learning Algorithms ◽  
Mapping Function ◽  
Learning Approaches ◽  
Autonomous Mobile Robot ◽  
Q Learning ◽  
Initial Values ◽  
Continuous State

Reinforcement learning approaches are attracting attention as a technique for constructing a trial-anderror mapping function between sensors and motors of an autonomous mobile robot. Conventional reinforcement learning approaches use a look-up table to express the mapping function between grid state and grid action spaces. The grid size greatly adversely affects the learning performance of reinforcement learning algorithms. To avoid this, researchers have proposed reinforcement learning algorithms using neural networks to express the mapping function between continuous state space and action. A designer, however, must set the number of middle neurons and initial values of weight parameters appropriately to improve the approximate accuracy of neural networks. This paper proposes a new method that automatically sets the number ofmiddle neurons and initial values of weight parameters based on the dimension number of the sensor space. The feasibility of proposed method is demonstrated using an autonomous mobile robot navigation problem and is evaluated by comparing it with two types of Q-learning as follows: Q-learning using RBF networks and Q-learning using neural networks whose parameters are set by a designer.

SENSE IT - student created water quality sensors

2011 ◽  
Author(s):  
L. Hotaling ◽  
R. Stolkin ◽  
S. Lowes ◽  
P. Chen ◽  
J. Bonner ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Quality Sensors

scholarly journals Personalized project recommendations: using reinforcement learning

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Faxin Qi ◽  
Xiangrong Tong ◽  
Lei Yu ◽  
Yingjie Wang
Keyword(s):  
Reinforcement Learning ◽  
User Behavior ◽  
Collaborative Work ◽  
Recursive Least Squares ◽  
The Internet ◽  
Dynamic Impact ◽  
Rls Algorithm ◽  
Trust Value ◽  
Q Learning ◽  
Actual Evaluation

AbstractWith the development of the Internet and the progress of human-centered computing (HCC), the mode of man-machine collaborative work has become more and more popular. Valuable information in the Internet, such as user behavior and social labels, is often provided by users. A recommendation based on trust is an important human-computer interaction recommendation application in a social network. However, previous studies generally assume that the trust value between users is static, unable to respond to the dynamic changes of user trust and preferences in a timely manner. In fact, after receiving the recommendation, there is a difference between actual evaluation and expected evaluation which is correlated with trust value. Based on the dynamics of trust and the changing process of trust between users, this paper proposes a trust boost method through reinforcement learning. Recursive least squares (RLS) algorithm is used to learn the dynamic impact of evaluation difference on user’s trust. In addition, a reinforcement learning method Deep Q-Learning (DQN) is studied to simulate the process of learning user’s preferences and boosting trust value. Experiments indicate that our method applied to recommendation systems could respond to the changes quickly on user’s preferences. Compared with other methods, our method has better accuracy on recommendation.

scholarly journals Integrating Production Planning with Truck-Dispatching Decisions through Reinforcement Learning While Managing Uncertainty

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 587
Author(s):  
Joao Pedro de Carvalho ◽  
Roussos Dimitrakopoulos
Keyword(s):  
Reinforcement Learning ◽  
Discrete Event ◽  
Mining Operations ◽  
Fixed Sequence ◽  
Q Learning ◽  
Reward Function ◽  
Copper Gold ◽  
Mining Complex ◽  
Learning Reinforcement ◽  
Operational Plan

This paper presents a new truck dispatching policy approach that is adaptive given different mining complex configurations in order to deliver supply material extracted by the shovels to the processors. The method aims to improve adherence to the operational plan and fleet utilization in a mining complex context. Several sources of operational uncertainty arising from the loading, hauling and dumping activities can influence the dispatching strategy. Given a fixed sequence of extraction of the mining blocks provided by the short-term plan, a discrete event simulator model emulates the interaction arising from these mining operations. The continuous repetition of this simulator and a reward function, associating a score value to each dispatching decision, generate sample experiences to train a deep Q-learning reinforcement learning model. The model learns from past dispatching experience, such that when a new task is required, a well-informed decision can be quickly taken. The approach is tested at a copper–gold mining complex, characterized by uncertainties in equipment performance and geological attributes, and the results show improvements in terms of production targets, metal production, and fleet management.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

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