Reinforcement Learning and deep reinforcement learning
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Reinforcement Learning: An Introduction Imitation Learning Lecture Slides from CMU Deep Reinforcement Learning Course We want a reinforcement learning agent to earn lots of reward The agent must prefer past actions that have been found to be effective at producing reward The agent must exploit what it already knows to obtain reward The agent must select untested actions to discover reward-producing actions The agent must explore actions to make better action selections in the future Trade-off between exploration and exploitation Reinforcement learning systems have 4 main elements: Policy Reward signal Value function Optional model of the environment Networks) Policy Gradient Methods (Finite Difference Policy Gradient, REINFORCE, Actor-Critic) Asynchronous Reinforcement Learning The reward signal defines the goal On each time step, the environment sends a single number called the reward to the reinforcement learning agent The agent’s objective is to maximise the total reward that it receives over the long run The reward signal is used to alter the policy Use the values to make and evaluate decisions Action choices are made based on value judgements Prefer actions that bring about states of highest value instead of highest reward Rewards are given directly by the environment Values must continually be re-estimated from the sequence of observations that an agent makes over its lifetime A model of the environment allows inferences to be made about how the environment will behave Example: Given a state and an action to be taken while in that state, the model could predict the next state and the next reward Models are used for planning, which means deciding on a course of action by considering possible future situations before they are experienced Model-based methods use models and planning. Think of this as modelling the dynamics p(s’ | s, a) Model-free methods learn exclusively from trial-and-error (i.e. no modelling of the environment) This presentation focuses on model-free methods
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25 introduction reinforcement_learning
Reinforcement learning is a method for learning behaviors through trial-and-error interactions with an environment. The goal is to maximize a numerical reward signal by discovering the actions that yield the most reward. The learner is not told which actions to take directly, but must instead determine which actions are best by trying them out. This document outlines reinforcement learning concepts like exploration versus exploitation, where exploration involves trying non-optimal actions to gain more information, while exploitation uses current knowledge to choose optimal actions. It also discusses formalisms like Markov decision processes and the tradeoff between maximizing short-term versus long-term rewards in reinforcement learning problems.
Introduction of Deep Reinforcement Learning
발표자: 곽동현(서울대 박사과정, 현 NAVER Clova)
강화학습(Reinforcement learning)의 개요 및 최근 Deep learning 기반의 RL 트렌드를 소개합니다.
발표영상:
http://tv.naver.com/v/2024376
https://youtu.be/dw0sHzE1oAc
Reinforcement Learning
1) Reinforcement Learning 개요
- Markov Decision Processes
- 기본적인 강화학습의 개념
2) Planning vs Reinforcement Learning
- Reinforcement Learning과 Planning의 차이
- Model Dynamics 학습
- TD(Temporal Difference) 기반의 Reinforcement Learning
3) Reinforcement Learning 실습
- 간단한 2차원 게임에서의 실습 리뷰
http://www.fastcampus.co.kr/data_workshop_dla/
How to formulate reinforcement learning in illustrative ways
This lecture introduces reinforcement learning and how to approach learning it. It discusses formulating the environment as a Markov decision process and defines important concepts like policy, value functions, returns, and the Bellman equation. The key ideas are that reinforcement learning involves optimizing a policy to maximize expected returns, and value functions are introduced to indirectly evaluate and improve the policy through dynamic programming methods like policy iteration and value iteration. Understanding these fundamental concepts through simple examples is emphasized as the starting point for learning reinforcement learning.
Shanghai deep learning meetup 4
The document summarizes discussions from several Deep Learning meetups in Shanghai. It provides an overview of reinforcement learning concepts like Markov Decision Processes and Q-learning. It then describes how Deep Q-Networks were used to solve Atari games by representing the Q-table with a deep neural network and using experience replay. Finally, it discusses benchmarks and platforms for reinforcement learning like OpenAI Gym and potential directions for the Deep Learning program.
Harm van Seijen, Research Scientist, Maluuba at MLconf SF 2016
1. The document discusses using deep reinforcement learning for dialogue systems. Deep reinforcement learning combines reinforcement learning with deep learning and can be applied to large, complex problems like dialogue systems.
2. A key challenge in training dialogue managers is the huge number of samples needed; this is addressed through using a user simulator trained on offline data. Deep reinforcement learning can learn directly from the belief state space used by dialogue systems.
3. Pre-training the deep reinforcement learning model on offline data makes the training more sample efficient for learning good dialogue policies.
Reinforcement learning
This document provides an introduction to reinforcement learning. It defines reinforcement learning as finding a policy that maximizes the sum of rewards by interacting with an environment. It discusses key concepts like Markov decision processes, value functions, temporal difference learning, Q-learning, and deep reinforcement learning. The document also provides examples of applications in games, robotics, economics and comparisons of model-based planning versus model-free reinforcement learning approaches.
Reinforcement Learning
This document provides an overview of reinforcement learning and some key algorithms used in artificial intelligence. It introduces reinforcement learning concepts like Markov decision processes, value functions, temporal difference learning methods like Q-learning and SARSA, and policy gradient methods. It also describes deep reinforcement learning techniques like deep Q-networks that combine reinforcement learning with deep neural networks. Deep Q-networks use experience replay and fixed length state representations to allow deep neural networks to approximate the Q-function and learn successful policies from high dimensional input like images.
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25 introduction reinforcement_learning
Reinforcement learning is a method for learning behaviors through trial-and-error interactions with an environment. The goal is to maximize a numerical reward signal by discovering the actions that yield the most reward. The learner is not told which actions to take directly, but must instead determine which actions are best by trying them out. This document outlines reinforcement learning concepts like exploration versus exploitation, where exploration involves trying non-optimal actions to gain more information, while exploitation uses current knowledge to choose optimal actions. It also discusses formalisms like Markov decision processes and the tradeoff between maximizing short-term versus long-term rewards in reinforcement learning problems.
Introduction of Deep Reinforcement Learning
발표자: 곽동현(서울대 박사과정, 현 NAVER Clova)
강화학습(Reinforcement learning)의 개요 및 최근 Deep learning 기반의 RL 트렌드를 소개합니다.
발표영상:
http://tv.naver.com/v/2024376
https://youtu.be/dw0sHzE1oAc
Reinforcement Learning
1) Reinforcement Learning 개요
- Markov Decision Processes
- 기본적인 강화학습의 개념
2) Planning vs Reinforcement Learning
- Reinforcement Learning과 Planning의 차이
- Model Dynamics 학습
- TD(Temporal Difference) 기반의 Reinforcement Learning
3) Reinforcement Learning 실습
- 간단한 2차원 게임에서의 실습 리뷰
http://www.fastcampus.co.kr/data_workshop_dla/
How to formulate reinforcement learning in illustrative ways
This lecture introduces reinforcement learning and how to approach learning it. It discusses formulating the environment as a Markov decision process and defines important concepts like policy, value functions, returns, and the Bellman equation. The key ideas are that reinforcement learning involves optimizing a policy to maximize expected returns, and value functions are introduced to indirectly evaluate and improve the policy through dynamic programming methods like policy iteration and value iteration. Understanding these fundamental concepts through simple examples is emphasized as the starting point for learning reinforcement learning.
Shanghai deep learning meetup 4
The document summarizes discussions from several Deep Learning meetups in Shanghai. It provides an overview of reinforcement learning concepts like Markov Decision Processes and Q-learning. It then describes how Deep Q-Networks were used to solve Atari games by representing the Q-table with a deep neural network and using experience replay. Finally, it discusses benchmarks and platforms for reinforcement learning like OpenAI Gym and potential directions for the Deep Learning program.
Harm van Seijen, Research Scientist, Maluuba at MLconf SF 2016
1. The document discusses using deep reinforcement learning for dialogue systems. Deep reinforcement learning combines reinforcement learning with deep learning and can be applied to large, complex problems like dialogue systems.
2. A key challenge in training dialogue managers is the huge number of samples needed; this is addressed through using a user simulator trained on offline data. Deep reinforcement learning can learn directly from the belief state space used by dialogue systems.
3. Pre-training the deep reinforcement learning model on offline data makes the training more sample efficient for learning good dialogue policies.
Reinforcement learning
This document provides an introduction to reinforcement learning. It defines reinforcement learning as finding a policy that maximizes the sum of rewards by interacting with an environment. It discusses key concepts like Markov decision processes, value functions, temporal difference learning, Q-learning, and deep reinforcement learning. The document also provides examples of applications in games, robotics, economics and comparisons of model-based planning versus model-free reinforcement learning approaches.
Reinforcement Learning
This document provides an overview of reinforcement learning and some key algorithms used in artificial intelligence. It introduces reinforcement learning concepts like Markov decision processes, value functions, temporal difference learning methods like Q-learning and SARSA, and policy gradient methods. It also describes deep reinforcement learning techniques like deep Q-networks that combine reinforcement learning with deep neural networks. Deep Q-networks use experience replay and fixed length state representations to allow deep neural networks to approximate the Q-function and learn successful policies from high dimensional input like images.
Deep reinforcement learning from scratch
1. The document provides an overview of deep reinforcement learning and the Deep Q-Network algorithm. It defines the key concepts of Markov Decision Processes including states, actions, rewards, and policies.
2. The Deep Q-Network uses a deep neural network as a function approximator to estimate the optimal action-value function. It employs experience replay and a separate target network to stabilize learning.
3. Experiments applying DQN to the Atari 2600 game Space Invaders are discussed, comparing different loss functions and optimizers. The standard DQN configuration with MSE loss and RMSProp performed best.
reinforcement learning in artificial intelligence
Reinforcement learning is a type of machine learning used to train algorithms to make sequences of decisions through trial-and-error interactions with an environment. It involves three main components: policies that define the agent's behavior, value functions that estimate the desirability of states and actions, and reinforcement learning algorithms like Q-learning that enable agents to learn from rewards and punishments. Reinforcement learning has applications in robotics, game playing, autonomous vehicles and more, though faces challenges around sample efficiency, exploration versus exploitation, and generalization. Future work includes more advanced applications, improved algorithms, and addressing ethical implications.
An introduction to reinforcement learning
This document provides an introduction and overview of reinforcement learning. It begins with a syllabus that outlines key topics such as Markov decision processes, dynamic programming, Monte Carlo methods, temporal difference learning, deep reinforcement learning, and active research areas. It then defines the key elements of reinforcement learning including policies, reward signals, value functions, and models of the environment. The document discusses the history and applications of reinforcement learning, highlighting seminal works in backgammon, helicopter control, Atari games, Go, and dialogue generation. It concludes by noting challenges in the field and prominent researchers contributing to its advancement.
acai01-updated.ppt
This document summarizes machine learning and inductive logic programming techniques for multi-agent systems. It discusses using machine learning for single agents and multi-agent systems, including inductive learning, reinforcement learning, and unsupervised learning. For multi-agent systems, it covers social awareness, communication, and role learning using techniques like Q-learning.
Reinforcement learning
1. Reinforcement learning involves an agent learning through trial-and-error interactions with an environment. The agent learns a policy for how to act by maximizing rewards.
2. The document outlines key elements of reinforcement learning including states, actions, rewards, value functions, and explores different methods for solving reinforcement learning problems including dynamic programming, Monte Carlo methods, and temporal difference learning.
3. Temporal difference learning combines the advantages of Monte Carlo methods and dynamic programming by allowing for incremental learning through bootstrapping predictions like dynamic programming while also learning directly from experience like Monte Carlo methods.
Unlocking Exploration: Self-Motivated Agents Thrive on Memory-Driven Curiosity
Despite remarkable successes in various domains such as robotics and games, Reinforcement Learning (RL) still struggles with exploration inefficiency. For example, in hard Atari games, state-of-the-art agents often require billions of trial actions, equivalent to years of practice, while a moderately skilled human player can achieve the same score in just a few hours of play. This contrast emerges from the difference in exploration strategies between humans, leveraging memory, intuition and experience, and current RL agents, primarily relying on random trials and errors. This tutorial reviews recent advances in enhancing RL exploration efficiency through intrinsic motivation or curiosity, allowing agents to navigate environments without external rewards. Unlike previous surveys, we analyze intrinsic motivation through a memory-centric perspective, drawing parallels between human and agent curiosity, and providing a memory-driven taxonomy of intrinsic motivation approaches.
The talk consists of three main parts. Part A provides a brief introduction to RL basics, delves into the historical context of the explore-exploit dilemma, and raises the challenge of exploration inefficiency. In Part B, we present a taxonomy of self-motivated agents leveraging deliberate, RAM-like, and replay memory models to compute surprise, novelty, and goal, respectively. Part C explores advanced topics, presenting recent methods using language models and causality for exploration. Whenever possible, case studies and hands-on coding demonstrations. will be presented.
Machine Learning: A gentle Introduction
Over the last years machine learning has become a hot topic. This is no surprise as the state of the art has reached levels that started to gain a lot of attention in the media: Self-driving cars, winning to Go game against top professional players and social media platforms that recognize your face in most online pictures.
This presentation provides an overall picture of machine learning and does not require any previous knowledge in the field. Sit back, relax and learn more about key concepts and recent advances of the field.
The demo part looks at two specific examples
* Pattern recognition with handwritten character recognition
* Natural language processing with neural word embeddings
At the end some less well known examples/topics will help to illustrate the current state of the art and should provide some food for thought regarding the future directions of this domain.
The slides have been used for a session with the Java User Group Switzerland: https://www.jug.ch/html/events/2017/machine_learning.html
Real-world Reinforcement Learning
Reinforcement learning is a machine learning technique where an agent learns to act by interacting with an environment. The agent takes actions and receives rewards, with the goal of maximizing total reward over time. Real-world reinforcement learning is challenging due to large state spaces and delayed rewards. However, it can be made more tractable by framing problems as contextual bandits, where rewards are immediate and state does not depend on past actions. Contextual bandits can then be solved using supervised learning techniques by addressing the partial information problem inherent to reinforcement learning.
Similarity learning
This presentation educate you through Similarity Learning, Goal of Similarity Learning, The Learning Algorithm, Experimental Setting, Quality Measures and Conclusions of Similarity Learning.
For more topics stay tuned with Learnbay.
Real-world Reinforcement Learning
This document discusses how reinforcement learning can be applied in the real world through simplifications and reductions.
Reinforcement learning is challenging to apply to real-world problems due to issues like large state spaces requiring massive amounts of data and delayed rewards. However, it can be simplified by focusing on problems with immediate rewards attributable to single actions and state independence. This reduced problem is known as contextual bandits.
Contextual bandits can be solved using machine learning reductions by treating the agent policy as a classifier and filling in missing reward information to allow supervised learning techniques to be applied. This involves reducing contextual bandits to supervised learning plus an exploration strategy, allowing any classification algorithm to be used as an "oracle learner" for the
An AHP-based Framework for Quality and Security Evaluation
Nowadays, there is a large diffusion of open and dynamic cooperative architectures that are based on services (SOA). In general, a customer is not only interested in service functionalities, but also in its quality (i.e. performance, cost, reliability, security and so on). In this scenario, models, techniques and tools supporting the effective selection of the service that provides the better quality are needed. In this paper, we propose an evaluation framework that includes a flexible quality meta-model for formalizing Customer and Provider views of quality, and a decisional model defining a systematic approach for comparing offered and requested quality of services. We will also illustrate the applicability of the framework in a Web Service scenario.
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New techniques for characterising damage in rock slopes.pdf
rock mass characterization and New techniques for characterising damage in rock slopes
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
Advanced control scheme of doubly fed induction generator for wind turbine us...
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CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Low power architecture of logic gates using adiabatic techniques
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
Heap Sort (SS).ppt FOR ENGINEERING GRADUATES, BCA, MCA, MTECH, BSC STUDENTS
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Contenu connexe
Similaire à Reinforcement Learning and deep reinforcement learning
Deep reinforcement learning from scratch
1. The document provides an overview of deep reinforcement learning and the Deep Q-Network algorithm. It defines the key concepts of Markov Decision Processes including states, actions, rewards, and policies.
2. The Deep Q-Network uses a deep neural network as a function approximator to estimate the optimal action-value function. It employs experience replay and a separate target network to stabilize learning.
3. Experiments applying DQN to the Atari 2600 game Space Invaders are discussed, comparing different loss functions and optimizers. The standard DQN configuration with MSE loss and RMSProp performed best.
reinforcement learning in artificial intelligence
Reinforcement learning is a type of machine learning used to train algorithms to make sequences of decisions through trial-and-error interactions with an environment. It involves three main components: policies that define the agent's behavior, value functions that estimate the desirability of states and actions, and reinforcement learning algorithms like Q-learning that enable agents to learn from rewards and punishments. Reinforcement learning has applications in robotics, game playing, autonomous vehicles and more, though faces challenges around sample efficiency, exploration versus exploitation, and generalization. Future work includes more advanced applications, improved algorithms, and addressing ethical implications.
An introduction to reinforcement learning
This document provides an introduction and overview of reinforcement learning. It begins with a syllabus that outlines key topics such as Markov decision processes, dynamic programming, Monte Carlo methods, temporal difference learning, deep reinforcement learning, and active research areas. It then defines the key elements of reinforcement learning including policies, reward signals, value functions, and models of the environment. The document discusses the history and applications of reinforcement learning, highlighting seminal works in backgammon, helicopter control, Atari games, Go, and dialogue generation. It concludes by noting challenges in the field and prominent researchers contributing to its advancement.
acai01-updated.ppt
This document summarizes machine learning and inductive logic programming techniques for multi-agent systems. It discusses using machine learning for single agents and multi-agent systems, including inductive learning, reinforcement learning, and unsupervised learning. For multi-agent systems, it covers social awareness, communication, and role learning using techniques like Q-learning.
Reinforcement learning
1. Reinforcement learning involves an agent learning through trial-and-error interactions with an environment. The agent learns a policy for how to act by maximizing rewards.
2. The document outlines key elements of reinforcement learning including states, actions, rewards, value functions, and explores different methods for solving reinforcement learning problems including dynamic programming, Monte Carlo methods, and temporal difference learning.
3. Temporal difference learning combines the advantages of Monte Carlo methods and dynamic programming by allowing for incremental learning through bootstrapping predictions like dynamic programming while also learning directly from experience like Monte Carlo methods.
Unlocking Exploration: Self-Motivated Agents Thrive on Memory-Driven Curiosity
Despite remarkable successes in various domains such as robotics and games, Reinforcement Learning (RL) still struggles with exploration inefficiency. For example, in hard Atari games, state-of-the-art agents often require billions of trial actions, equivalent to years of practice, while a moderately skilled human player can achieve the same score in just a few hours of play. This contrast emerges from the difference in exploration strategies between humans, leveraging memory, intuition and experience, and current RL agents, primarily relying on random trials and errors. This tutorial reviews recent advances in enhancing RL exploration efficiency through intrinsic motivation or curiosity, allowing agents to navigate environments without external rewards. Unlike previous surveys, we analyze intrinsic motivation through a memory-centric perspective, drawing parallels between human and agent curiosity, and providing a memory-driven taxonomy of intrinsic motivation approaches.
The talk consists of three main parts. Part A provides a brief introduction to RL basics, delves into the historical context of the explore-exploit dilemma, and raises the challenge of exploration inefficiency. In Part B, we present a taxonomy of self-motivated agents leveraging deliberate, RAM-like, and replay memory models to compute surprise, novelty, and goal, respectively. Part C explores advanced topics, presenting recent methods using language models and causality for exploration. Whenever possible, case studies and hands-on coding demonstrations. will be presented.
Machine Learning: A gentle Introduction
Over the last years machine learning has become a hot topic. This is no surprise as the state of the art has reached levels that started to gain a lot of attention in the media: Self-driving cars, winning to Go game against top professional players and social media platforms that recognize your face in most online pictures.
This presentation provides an overall picture of machine learning and does not require any previous knowledge in the field. Sit back, relax and learn more about key concepts and recent advances of the field.
The demo part looks at two specific examples
* Pattern recognition with handwritten character recognition
* Natural language processing with neural word embeddings
At the end some less well known examples/topics will help to illustrate the current state of the art and should provide some food for thought regarding the future directions of this domain.
The slides have been used for a session with the Java User Group Switzerland: https://www.jug.ch/html/events/2017/machine_learning.html
Real-world Reinforcement Learning
Reinforcement learning is a machine learning technique where an agent learns to act by interacting with an environment. The agent takes actions and receives rewards, with the goal of maximizing total reward over time. Real-world reinforcement learning is challenging due to large state spaces and delayed rewards. However, it can be made more tractable by framing problems as contextual bandits, where rewards are immediate and state does not depend on past actions. Contextual bandits can then be solved using supervised learning techniques by addressing the partial information problem inherent to reinforcement learning.
Similarity learning
This presentation educate you through Similarity Learning, Goal of Similarity Learning, The Learning Algorithm, Experimental Setting, Quality Measures and Conclusions of Similarity Learning.
For more topics stay tuned with Learnbay.
Real-world Reinforcement Learning
This document discusses how reinforcement learning can be applied in the real world through simplifications and reductions.
Reinforcement learning is challenging to apply to real-world problems due to issues like large state spaces requiring massive amounts of data and delayed rewards. However, it can be simplified by focusing on problems with immediate rewards attributable to single actions and state independence. This reduced problem is known as contextual bandits.
Contextual bandits can be solved using machine learning reductions by treating the agent policy as a classifier and filling in missing reward information to allow supervised learning techniques to be applied. This involves reducing contextual bandits to supervised learning plus an exploration strategy, allowing any classification algorithm to be used as an "oracle learner" for the
An AHP-based Framework for Quality and Security Evaluation
Nowadays, there is a large diffusion of open and dynamic cooperative architectures that are based on services (SOA). In general, a customer is not only interested in service functionalities, but also in its quality (i.e. performance, cost, reliability, security and so on). In this scenario, models, techniques and tools supporting the effective selection of the service that provides the better quality are needed. In this paper, we propose an evaluation framework that includes a flexible quality meta-model for formalizing Customer and Provider views of quality, and a decisional model defining a systematic approach for comparing offered and requested quality of services. We will also illustrate the applicability of the framework in a Web Service scenario.
Similaire à Reinforcement Learning and deep reinforcement learning (11)
Unlocking Exploration: Self-Motivated Agents Thrive on Memory-Driven Curiosity
Unlocking Exploration: Self-Motivated Agents Thrive on Memory-Driven Curiosity
An AHP-based Framework for Quality and Security Evaluation
An AHP-based Framework for Quality and Security Evaluation
Dernier
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单专业办理
UMich毕业证原版定制【微信：176555708】【密歇根大学|安娜堡分校毕业证成绩单-学位证】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
学历顾问：微信：176555708
New techniques for characterising damage in rock slopes.pdf
rock mass characterization and New techniques for characterising damage in rock slopes
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
Advanced control scheme of doubly fed induction generator for wind turbine us...
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Low power architecture of logic gates using adiabatic techniques
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
Heap Sort (SS).ppt FOR ENGINEERING GRADUATES, BCA, MCA, MTECH, BSC STUDENTS
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
原版一模一样【微信：741003700 】【(csu毕业证书)查尔斯特大学毕业证硕士学历】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Literature Review Basics and Understanding Reference Management.pptx
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
一比一原版(UC Berkeley毕业证)加利福尼亚大学|伯克利分校毕业证成绩单专业办理
UC Berkeley毕业证原版定制【微信：176555708】【加利福尼亚大学|伯克利分校毕业证成绩单-学位证】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
学历顾问：微信：176555708
[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf
Kinematics 11th jpp- 01. ( Solved ) unacademy namo kaul on 14th may...
Building Electrical System Design & Installation
Guide for Building Electrical System Design & Installation
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单专业办理
IIT毕业证原版定制【微信：176555708】【伊利诺伊理工大学毕业证成绩单-学位证】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
学历顾问：微信：176555708
Dernier (20)
New techniques for characterising damage in rock slopes.pdf
New techniques for characterising damage in rock slopes.pdf
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
ACRP 4-09 Risk Assessment Method to Support Modification of Airfield Separat...
ACRP 4-09 Risk Assessment Method to Support Modification of Airfield Separat...
Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
Low power architecture of logic gates using adiabatic techniques
Low power architecture of logic gates using adiabatic techniques
Heap Sort (SS).ppt FOR ENGINEERING GRADUATES, BCA, MCA, MTECH, BSC STUDENTS
Heap Sort (SS).ppt FOR ENGINEERING GRADUATES, BCA, MCA, MTECH, BSC STUDENTS
Literature Review Basics and Understanding Reference Management.pptx
Literature Review Basics and Understanding Reference Management.pptx
[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf
[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024)
Reinforcement Learning and deep reinforcement learning
- 2. Overview
- 3. Introduction to Reinforcement Learning Chapter 1 – Reinforcement Learning: An Introduction Imitation Learning Lecture Slides from CMU Deep Reinforcement Learning Course
- 4. What is Reinforcement Learning?
- 7. Policy
- 14. Reward Design
- 15. What is Deep Reinforcement Learning?
- 16. Finite Markov Decision Processes Chapter 3 – Reinforcement Learning: An Introduction
- 17. Markov Decision Process (MDP)
- 18. Time Discounting
- 21. Action Selection
- 22. MDP Dynamics
- 24. Expected Rewards
- 30. Optimality
- 31. Temporal-Difference Learning Chapter 6 – Reinforcement Learning: An Introduction Playing Atari with Deep Reinforcement Learning Asynchronous Methods for Deep Reinforcement Learning David Silver’s Tutorial on Deep Reinforcement Learning
- 32. What is TD learning?
- 34. Update Rule for TD(0)
- 37. SARSA – On-policy TD Control
- 39. SARSA Algorithm
- 40. Q-learning – Off-policy TD Control
- 44. Q-Networks
- 48. Loss Function Gradient Derivation
- 49. DQN Algorithm
- 50. Comments
- 51. Policy Gradient Methods Chapter 13 – Reinforcement Learning: An Introduction Policy Gradient Lecture Slides from David Silver’s Reinforcement Learning Course David Silver’s Tutorial on Deep Reinforcement Learning
- 52. What are Policy Gradient Methods?
- 54. Notation
- 56. Types of Policy Gradient Method
- 57. Finite Difference Policy Gradient
- 58. REINFORCE: Monte Carlo Policy Gradient
- 62. One-step Actor-Critic Update Rules
- 64. Asynchronous Reinforcement Learning Asynchronous Methods for Deep Reinforcement Learning
- 65. What is Asynchronous Reinforcement Learning?
- 66. Parallelism (1)
- 67. Parallelism (2)
- 71. Exploration
- 72. Asynchronous one-step Q-learning Algorithm
- 75. n-step Returns
- 76. Asynchronous n-step Q-learning Algorithm
- 77. A3C
- 79. A3C Algorithm
- 80. Summary