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.

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

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