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Reinforcement Learning Course

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  • The basics of Reinforcement Learning Training methods such as value/policy iteration, q-learning, policy gradient, and others employing deep neural networks will be covered in this course.
  • This Reinforcement Learning Course will teach you how to use statistical learning techniques to create agents that perform explicit actions and interact with the environment.
  • Reinforcement Learning Online Course is one of the most popular academic subjects, and its popularity continues to grow.
  • Reinforcement learning Online Training is a branch of machine learning concerned with how intelligent agents should operate in a given environment to maximize the concept of cumulative reward.
  • Learners will comprehend the foundations of much of modern probabilistic artificial intelligence (AI) by the end of this Specialization and will be prepared to take more advanced courses or apply AI tools and ideas to real-world issues.
  • You should have taken a graduate-level machine-learning course and have had some exposure to reinforcement learning through earlier courses or a computer science seminar before taking this course.
  • This Reinforcement Learning Course will teach you the fundamentals of Reinforcement Learning, as well as the algorithms that underpin both classic and modern RL methods. You will be able to use RL to solve real-world problems after completing this course.
  • Concepts: Introduction to Schema, Changing Datatypes Of Elements In Schema, Validating Maps (Schema), Debugging & Exceptions, Flat Files, Power shell Scripting.
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Course Objectives

Typically, as you advance in machine learning, your curriculum will take about 6 months to finish in total. If you study for a minimum of 6 months.. If you stick to this plan, 6 months will be plenty for you. But only if you possess strong mathematical and analytical abilities.

No, it's very easy to learn reinforcement Reinforcement Learning Onling Course faces a number of predicaments that are similar to those faced by managed and unsupervised methods, but it also faces its own set of unique and highly complex tests, such as difficult training/design setup and queries related to the balance of exploration and reinforcement.

Experts say deep Reinforcement Learning Certification Training is at the cutting edge right now and has now arrived at a position where it can be utilized in real-world applications. They also predict that relocating it will have a significant influence on AI progress, bringing researchers closer to Artificial General Intelligence (AGI).
  • Top Reinforcement learning Companies
  • Insilico Medicine Private Company.
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Although remarkable, reinforcement learning has yet to find broad acceptance or real-world success outside of playing games and escaping mazes. Indeed, even for relatively easy issues, reinforcement learning necessitates extensive training, which can take hours, days, or even weeks.

The goal of reinforcement learning is for the agent to discover an optimal (or nearly optimal) strategy that maximizes the "reward function" or another user-provided reinforcement signal that accumulates from the immediate rewards. This appears to be comparable to what happens in animal psychology. Decisions are made via reinforcement learning. It becomes feasible for an intelligent system to try new actions or approaches, alter course when failures occur (or negative reinforcement), and build on successes by constructing a simulation of a whole company or system (or positive reinforcement).

  • TensorFlow.
  • Keras.
  • DeepMind Lab.
  • Pytorch.
  • Summing It Up.
  • OpenAI Gym.

What are the prerequisites and requirements for this Reinforcement Learning course?

You should have attended a graduate-level machine-learning course and have had some exposure to reinforcement learning from a previous computer science course or seminar before taking this course. In addition, you will be programming in Java extensively throughout this course.

What is the average salary for a Reinforcement Learning expert?

Reinforcement engineers earn an average yearly income of Rs.671,548. Machine learning engineers with less than a year of experience earn around 500,000 rupees per year, making them one of the highest-paid entry-level jobs in India.

What are the certificates that are worthwhile in Reinforcement Learning?

  • Certifications for specific roles
  • Certifications in Human Resources (PHR, SPHR, SHRM)
  • Certifications in Project Management (PMP)
  • Certifications in Sales (Challenger Sales, Spin Selling, Sandler Training)
  • Certifications in Help Desk/Desktop Analyst (A+, Network+)
  • Certifications for Networks (CCNA, CCNP, CCIE)
  • Salesforce.

Why Should I Take a Reinforcement Learning Course?

You'll learn how to utilize supervised and unsupervised machine learning algorithms to analyze and forecast data, as well as how to use reinforcement learning to train an agent to interact with an environment and optimize its reward. When there is a dearth of domain expertise for feature introspection, Deep Learning approaches outperform others since feature engineering is less of a concern. When it comes to difficult tasks like picture classification, natural language processing, and speech recognition, deep learning truly shines.

What does the future hold for Reinforcement Learning developers?

The future of Reinforcement Learning appears bright. Top companies are staying with java and Reinforcement Learning as present and future hot technologies. Reinforcement Learning has therefore become a basic language, with Reinforcement Learning being utilized in research, development, and manufacturing. Reinforcement Learning is a technique that is utilized in a variety of situations.
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Overview of Reinforcement Learning Online Course

The basics of strengthening learning will assist you (RL). In addition to algorithms and theory, discover some essential tips and strategies to learn stabilization and how such methods can be applied to large-scale problems together with deep neural networks. You will be exposed to strengthening learning, an area of Reinforcement Learning Online Training. You will study processes of Markov's decision, bandit algorithms, dynamic programming, and time differences (TD). The value function, the Bellman equation, and the iteration of value will be introduced. You'll also study approaches for policy gradients. In an uncertain world, you will learn to make decisions.

Self-study Courses for self-directed training are designed to allow participants to start scheduled training and review exercises to strengthen learning at their convenience. You will learn the Reinforcement Learning Online Course to complete tasks to improve learning outcomes, projects, and other activities. Professionals need to be aware of the foundations of many of the modern probabilistic artificial intelligence (AI) and want to study or use AI tools and ideas for real-life situations. In order to grasp the fundamentals of refurbishment education as provided by world-famous scientists at the Faculty of Sciences, this content will focus on "small-scale" challenges.


Additional Info

Different Practical Reinforcement Learning Applications :

1. In industrial automation, RL can be utilized in robots.

2. RL for machine and data processing can be employed

3. RL can be used to develop training systems that give customized education and resources according to students' needs.

In the following instances, RL can be applied in big environments :

1. An environmental model is known, but there is no analytical solution.

2. Only an environment simulation model is provided (the subject of simulation-based optimization)

3. Interacting with it is the only method to gather knowledge of the surroundings.

Who Should Learn this Reinforcement Learning Course?

This course is for mid-career workers who are active in reinforcement learning or would like to learn more about it. These techniques will be implemented in a range of disciplines such as robotics, car manufacturing, urban planning and design, government and military logistics, research and technology, retail sector, finance, healthcare, and pharmaceutical industries. Relevant job titles include, but are not limited to :

  • Research Scientist
  • Machine Learning Engineer
  • Software Engineer
  • Data Scientist
  • Data Analyst
  • Automation Engineer
  • CTO
  • Product Manager
  • Program Manager

Highlights Of Reinforcement :

Recent AI research has led to powerful deep enhancement learning approaches. Deep enhancement learning appears to be intrinsic in psychology and neuroscience in their coupling of representational learning with reward-guided behavior. One criticism was that profound enhancement processes require vast quantities of training data, suggesting that these algorithms can essentially differ from those behind human education.

While this worry pertains to the initial wave of deep-RL techniques, following AI work has provided methods for the quicker and more effective learning of deep-RL systems. Episodic memory and meta-learning are two very fascinating and promising methodologies focus. As well as their interest in AI techniques and in psychology and neuroscience, profound methods of RL use episodic memory and meta-learning. The basic link between rapid and slow learning forms is a subtle but critically important insight that these strategies bring to focus.

Reinforcement Learning Application areas :

  • Games :

    RL is today so well recognized as the typical algorithm used to solve various games and to obtain a superhuman performance. AlphaGo and AlphaGo Zero must be the most renowned. The Monte Carlo Tree Value Research and Value (MCTS) network has provided AlphaGo, trained in innumerable games for human beings, with superhuman performances. The researchers, however, tried to approach RL more clearly – to train it from the beginning. The researchers left their new AI, AlphaGo Zero to beat AlphaGo 100–0 all by themselves.

  • Custom recommendations :

    The news suggestions have always been confronted with various obstacles, including fast-changing news dynamics, users who readily pull, and a click rate that does not match user retention rates. In the publication entitled "DRN: A Deep Reinforcement Learning Framework for News Recommendation," Guanjie et al. applied RL to the news recommendations system for addressing issues.

    They have created four kinds of resources :

    A. user resources

    B. context resources such as environmental State sources,

    C. user news resources, and

    D. news sources such as action resources.

    They have created four categories of resources in practice.

  • Robotics :

    The Deep Q-Network (DQN) has four resources for the calculation of the Q value. In order to propose a news list, a user clicked on the news and the news was included in the prize earned by the RL agent. The author also used various strategies, including memory repeat, survival models, Dueling bandit gradient descent, and more, to solve different challenge problems. Computer cluster resource management Designing algorithms to allocate limited resources to multiple activities is tough and requires human-generated heuristics.

  • Resource management :

    The article entitled "Resource management with extensive reinforcement learning" shows how to automatically use RL to learn how to distribute computer resources to ongoing jobs so that the (task) delay is minimized. The state-space was defined as the current allocation of resources and the employee resource profile. They employed a method to enable the agent to select more than one action at any time stage in the action area. The reward for all work in the system was (-1/work duration).

    The REINFORCE Algorithm then is paired with the baseline value to generate policy gradients and identify appropriate policy parameters to distribute. DQN was utilized by the authors to learn the {state, actions} value of Q pairings. Robotics The application of RL in robots is astounding. This paper with the results of RL robotic research is advisable to read. In this other project, scientists have trained a robot to learn policies to map the activities of the robot with raw video footage. RGB images were transmitted to a CNN and engine torques were the outputs. The policy research component RL was directed towards the generation of training data from its national distribution. Setup of Web Systems.

  • Web Systems Configuration :

    The system configuration was the state-space; for each parameter, the space for action {increase, decline, maintain} was. The prize is defined as the difference between the reaction time intended and the reaction time measured. In order to execute the task, the author employed the Q-learning algorithm. While the authors have used certain other techniques, such as policy initialization in order to address the large state space and the problem's computational complexity, it is assumed the pioneering work prepared the way for future research into this field rather than the possible combinations of the RL and neural network. In order to optimize chemical reactions, RL chemistry can also be used.

    In the article "Optimizing chemical reactions with deep enhancement learning," the researchers demonstrated that their model trounced a state-of-the-art technology and has been generated into several underlying mechanisms.

  • Chemistry :

    In conjunction with LSTM to model the policy function, agent RL optimized the chemical reaction through the Markov decision-making process (MDP) characterized by {S, A, P, R} which provided for S the set of experimental conditions (e.g. temperature, pH, etc.); A was the set of all possible measures that could affect experimental conditions. It is a fantastic instance for showcasing how RL in generally stable environments may save time, test, and error.

  • Auctions and Advertising :

    Auctions Group auction and advertising researchers published the essay "Effective Time A Their cluster-based multi-agent distribution system (DCMAB) has been reported to have produced good results and consequently wants to test the lives of the Taobao platform. The Taobao ad platform is generally used by marketers to offer to advertise to customers. For many agents, this can be an issue because traders are bidding each other and their actions are interlinked.

  • Deep Learning :

    The article separated merchants and customers into various groups to reduce computational complexity. The state-space of the agents indicated the cost-revenue status of the agents, the space for action was a (continuous) offer and the prize was the income of the client class. Profound education In recent times increasingly attempts at combining RL and other profound learning architectures have demonstrated outstanding outcomes. Deepmind's pioneering work in combining CNN with RL is one of RL's most important responsibilities. In doing this, the agent can "see" and then learn to interact in the environment using high-dimensional sensors.

    RL and RNN are other combinations that people use to experiment with new concepts. RNN is a sort of "memory" neural network. RNN allows agents the option to store objects in combination with RL. For instance, LSTM and RL have been coupled to form a deep recurrent Q network (DRQN). RNN and RL are also utilized for issue solving.

When should you use RL?

Increased rewards depending on the decisions taken; interactions with the environment can be learned at all times continually. We have good rewards and consequences for wrong actions with every right action. This form of learning in the business can contribute to optimizing processes, simulations, monitoring, maintenance, and autonomous system control.

Various criteria can be utilized to decide where strengthening learning should be applied :

1. If you wish to run some simulations because a certain process is complex or perhaps dangerous.

2. Increasing the number of human analysts and field specialists on a given subject. Instead of discovering the ideal method, this type of technique might emulate human thinking.

3. With each encounter, you can calibrate correctly, if you have a good reward definition for the learning process so that you have more benefits than negatives.

4. If you have minimal information on a specific topic.

5. Reinforcement learning is applied in several sectors in addition to industry, such as education, health care, finance, and the imagination

Responsibilities of a Machine Learning Engineer :

  • To explore and convert prototypes of data science.
  • Machine Learning Systems and Systems to create and develop.
  • To analyze statistics and to perfect the models with test results.
  • For training, purposes to identify available data sets online.
  • ML systems and models should and should be trained and re-trained.
  • Extend and enhance current library and ML frameworks.
  • Developing customer/client machine learning applications.
  • To investigate, test, and build appropriate ML algorithms and tools.
  • To examine and classify the problem-solving capabilities of the ML algorithms according to their probabilities for success.

Salary Perspective :

Salary for Skill in India: Reinforcement Learning 150K. There is a great demand for machine learning but firms need the right skills from humans. The demand is always great for these engineers. There's no end to the list. This is the main reason why machine learning wages are so expensive in India. The demand is growing. Moreover, the better the experience, the higher the wage. According to Payscale, the average machine learning wage in India is around Rs. 686K per year, including bonuses and profit shares.

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Key Features

ACTE offers Reinforcement Learning Training in more than 27+ branches with expert trainers. Here are the key features,

  • 40 Hours Course Duration
  • 100% Job Oriented Training
  • Industry Expert Faculties
  • Free Demo Class Available
  • Completed 500+ Batches
  • Certification Guidance

Authorized Partners

ACTE TRAINING INSTITUTE PVT LTD is the unique Authorised Oracle Partner, Authorised Microsoft Partner, Authorised Pearson Vue Exam Center, Authorised PSI Exam Center, Authorised Partner Of AWS and National Institute of Education (nie) Singapore.


Syllabus of Reinforcement Learning Course
Module 1: Introduction to Reinforcement Learning
  • Branches of Machine Learning
  • What is Reinforcement Learning?
  • The Reinforcement Learning Process
  • Elements of Reinforcement Learning
  • RL Agent Taxonomy
  • Reinforcement Learning Problem
  • Introduction to OpenAI Gym
Module 2: Bandit Algorithms and Markov Decision Process
  • Bandit Algorithms
  • Markov Process
  • Markov Reward Process
  • Markov Decision Process
Module 3: Dynamic Programming & Temporal Difference Methods
  • Introduction to Dynamic Programming
  • Dynamic Programming Algorithms
  • Monte Carlo Methods
  • Temporal Difference Learning Methods
Module 4: Deep Q Learning
  • Policy Gradients
  • Policy Gradients using TensorFlow
  • Deep Q learning
  • Q learning with replay buffers, target networks, and CNN
Module 5: In-class Project
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Get Hands-on Knowledge about Real-Time Reinforcement Learning Projects

Project 1
Long Text Generation with LeakGAN

This is a very interesting reinforcement learning project that generates lengthy texts with the assist of generative antagonistic networks for producing favored results.

Project 2
Self-Driving Truck Simulator with Reinforcement Learning

This is made achievable with the aid of using the reinforcement learning-powered AI version that’s made successful to steer, accelerate, and brake the truck according to requirement.

Project 3
City Bike Rebalancing Problem

This Reinforcement project appears to clear up the motorcycle rebalancing hassle faced with the aid of using Citi Bike in a city like New York.

Project 4
Connect4 Game Playing by AlphaGo Zero Method

The most famous use of Reinforcement Learning is to make the agent discover ways to play specific games.

Our Engaging Partner for Placements

All students and experts who've finished ACTE Reinforcement Learning classroom or online teaching are eligible for placement. Some of our students are hired with the aid of using the companies indexed below.
  • We paintings with prestigious groups such as KRISHPAR TECHNOLOGIES PRIVATE LIMITED, Toyota Connected, Bert Labs, Global Talent Pool, Adecco India Private Limited, CareerXperts Consulting, and others. It allows us to position our college students in primary MNCs everywhere in the world.
  • After 70% of the Reinforcement Learning course content, we are able to organize an interview with college students and put together them for the F2F interaction.
  • Our Reinforcement Learning course allows students to increase their curriculum primarily based totally on the modern necessities of the organization.
  • We provide a specialized help group of workers for placement to assist college students in steady their placement on their needs.
  • In order to find out the GAP, we will behavior Mock examinations and Mock Interviews.
  • We provide regular wrap-ups of prior Placement Training seminars to help you improve your skills and memory.

Get Certified By Reinforcement Learning & Industry Recognized ACTE Certificate

Acte Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher's as well as corporate trainees.

Our certification at Acte is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC's of the world. The certification is only provided after successful completion of our training and practical based projects.

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Our Excellent Reinforcement Learning Course Trainers

  • Our Reinforcement Learning Online Course has certificated professionals with over 9+years of revel in their respective regions and work with the Top MNCs now.
  • Since all functioning Trainers are Reinforcement Learning Course specialists, they have got a variety of real tasks, they're education tasks which might be utilized by those running shoes.
  • All of our Trainers are hired through organizations including KRISHPAR TECHNOLOGIES PRIVATE LIMITED, Toyota Connected, Bert Labs, Global Talent Pool, Adecco India Private Limited, Career Xperts Consulting, and others.
  • To offer the final Reinforcement Learning Course to students, our instructors are enterprise specialists and difficulty count number professionals who've mastered the use of applications.
  • We were given numerous essential awards for Reinforcement Learning Course Training in Kolkata from famous IT firms.
  • We provide a specialized assist workforce for placement to assist students in steady their placement on their needs.

Reinforcement Learning Course Reviews

Our ACTE Reviews are listed here. Reviews of our students who completed their training with us and left their reviews in public portals and our primary website of ACTE & Video Reviews.



I underwent Reinforcement Learning training in ACTE, Porur. The training session was good. My tutor Mr.Anbu have been outstanding. I liked the sessions taught by him who is an experienced faculty. Each and every topic is explained very clearly. Materials provided by him were useful. He is really good with his training and has the best content with him for the training which is really useful for a fresher like me to learn.

Dinesh Karthik


Good Institute for getting your basics right in any course, Thanks to Prabhu sir for training me for Reinforcement Learning has around 10+ years of experience in Reinforcement Learning and covers all the real time scenario's in the classes



I have enrolled for Reinforcement Learning course in ACTE, Chennai It is a very nice experience. Trainer is very good and talented. All the concepts are thoroughly explained by the time you don't understand. Facilities are good. There is the provision of paying fees in instalment. Hence overall it's nice to choose



It was a great learning experience in ACTE, Banglore. The entire course structure designed for its students, the teaching methodology, as well as placement assistance, is really good. ACTE helped me a lot to get my first job. Had a wonderful opportunity to learn under the guidance of dedicated faculty team headed and gain knowledge in the field of Reinforcement Learning . I would recommend ACTE to people who are interested to learn Reinforcement Learning .



Very motivational environment. Best way to teach. Really appreciate the efforts they put from there side to increase the knowledge and development of students. Thanks, ACTE

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Reinforcement Learning Course FAQs

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Call now: +91 93833 99991 and know the exciting offers available for you!
  • ACTE is the Legend in offering placement to the students. Please visit our Placed Students List on our website
  • We have strong relationship with over 700+ Top MNCs like SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc.
  • More than 3500+ students placed in last year in India & Globally
  • ACTE conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
  • 85% percent placement record
  • Our Placement Cell support you till you get placed in better MNC
  • Please Visit Your Student Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions
    ACTE Gives Certificate For Completing A Course
  • Certification is Accredited by all major Global Companies
  • ACTE is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (NIE) Singapore
  • The entire Reinforcement Learning training has been built around Real Time Implementation
  • You Get Hands-on Experience with Industry Projects, Hackathons & lab sessions which will help you to Build your Project Portfolio
  • GitHub repository and Showcase to Recruiters in Interviews & Get Placed
All the instructors at ACTE are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by ACTE for providing an awesome learning experience.
No worries. ACTE assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.
We offer this course in “Class Room, One to One Training, Fast Track, Customized Training & Online Training” mode. Through this way you won’t mess anything in your real-life schedule.

Why Should I Learn Reinforcement Learning Course At ACTE?

  • Reinforcement Learning Course in ACTE is designed & conducted by Reinforcement Learning experts with 10+ years of experience in the Reinforcement Learning domain
  • Only institution in India with the right blend of theory & practical sessions
  • In-depth Course coverage for 60+ Hours
  • More than 50,000+ students trust ACTE
  • Affordable fees keeping students and IT working professionals in mind
  • Course timings designed to suit working professionals and students
  • Interview tips and training
  • Resume building support
  • Real-time projects and case studies
Yes We Provide Lifetime Access for Student’s Portal Study Materials, Videos & Top MNC Interview Question.
You will receive ACTE globally recognized course completion certification Along with National Institute of Education (NIE), Singapore.
We have been in the training field for close to a decade now. We set up our operations in the year 2009 by a group of IT veterans to offer world class IT training & we have trained over 50,000+ aspirants to well-employed IT professionals in various IT companies.
We at ACTE believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics. Therefore, we restrict the size of each Reinforcement Learning batch to 5 or 6 members
Our courseware is designed to give a hands-on approach to the students in Reinforcement Learning . The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
You can contact our support number at +91 93800 99996 / Directly can do by's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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