AI Course Fundamental | Reinforcement Learning by Diljeet Singh | Learn Smarter
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Academics

Grades

Grade 8 Grade 9 Grade 10 Grade 11 Grade 12

Curriculum

CBSE ICSE IB
Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Professional Courses
Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.

games
Typing
Memory
Math
English Adventures
Knowledge
Reinforcement Learning

Reinforcement Learning (RL) is a machine learning paradigm where agents learn to make decisions through interaction with environments, receiving rewards or penalties. Key concepts include rewards, policies, and value functions essential for guiding the agent's behavior. Q-learning and deep Q-networks represent significant advancements in RL, enabling effective learning in complex tasks like robotics and gaming. Mastery of RL principles facilitates the development of autonomous systems that improve decision-making through experience.

Enroll to start learning

You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.

Sections

  • 10

    Reinforcement Learning

    Reinforcement Learning (RL) is a machine learning paradigm that enables agents to learn how to make decisions through rewards and penalties by interacting with their environment.

    Learning Practice

  • 10.1

    Introduction To Reinforcement Learning

    Reinforcement Learning (RL) enables agents to learn decision-making through rewards and penalties from their environment, striving to maximize cumulative rewards.

    Learning Practice

  • 10.2

    Rewards, Policies, And Value Functions

    This section discusses the fundamental concepts of rewards, policies, and value functions in reinforcement learning, which guide an agent's learning process.

    Learning Practice

  • 10.2.1

    Rewards

    Rewards are scalar signals that guide an agent's decision-making in reinforcement learning by encouraging desirable behaviors.

    Learning Practice

  • 10.2.2

    Policies

    Policies dictate an agent's actions in reinforcement learning by mapping states to actions.

    Learning Practice

  • 10.2.3

    Value Functions

    Value functions provide a measurement for how beneficial a specific state or action is within reinforcement learning.

    Learning Practice

  • 10.3

    Q-Learning And Deep Q-Networks

    Q-Learning is a model-free reinforcement learning algorithm that learns optimal action values, and Deep Q-Networks extend this by using neural networks to handle larger state spaces.

    Learning Practice

  • 10.3.1

    Q-Learning

    Q-Learning is a model-free reinforcement learning algorithm that helps an agent learn the optimal action-value function through trial and error.

    Learning Practice

  • 10.3.2

    Deep Q-Networks (Dqn)

    Deep Q-Networks (DQN) integrate Q-learning with deep neural networks to manage larger state spaces and improve learning efficiency.

    Learning Practice

  • 10.4

    Applications In Robotics And Gaming

    This section highlights how reinforcement learning (RL) is applied in robotics and gaming.

    Learning Practice

  • 10.4.1

    Robotics

    Reinforcement Learning (RL) applications in robotics empower robots to learn and adapt to various tasks in dynamic and uncertain environments.

    Learning Practice

  • 10.4.2

    Gaming

    Reinforcement Learning algorithms significantly enhance gameplay strategies, achieving superhuman levels in various games.

    Learning Practice

  • 10.5

    Conclusion

    The conclusion emphasizes the significance of Reinforcement Learning as a framework for decision-making in uncertain environments.

    Learning Practice

References

Chapter 10_ Reinforcement Learning.pdf

Class Notes

Memorization

What we have learnt

  • Reinforcement Learning is a...
  • Policies define the behavio...
  • Q-learning and Deep Q-Netwo...

Final Test

Revision Tests