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Home » Technology » AI / Machine Learning » Deep Reinforcement Learning

Course Skill Level:

Foundational

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    DERELEL21E09

Who should attend & recommended skills:

Those with Python experience and basic Linux & deep learning algorithms experience

Who should attend & recommended skills

  • This course is geared for Python developers, analysts or others who want to build machine learning systems that explore and learn based on the responses of the environment. A powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You’ll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.
  • Skill-level: Foundation-level Deep Reinforcement Learning skills for Intermediate skilled team members. This is not a basic class.
  • Deep Learning Algorithms: Basic (1-2 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Reinforcement Learning: Not required

About this course

Deep Reinforcement Learning is a beautifully balanced approach to teaching, offering numerous large and small examples, annotated diagrams and code, engaging exercises, and skillfully crafted writing. You’ll explore, discover, and learn as you lock in the ins and outs of reinforcement learning, neural networks, and AI agents. You will go from small grid world environments and some of the foundational algorithms to some of the most challenging environments out there today and cutting-edge techniques to solve these environments.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Deep Reinforcement expert instructor, students will learn about and explore:
  • The ins and outs of reinforcement learning, neural networks, and AI agents
  • Going from small grid world environments and some of the foundational algorithms to some of the most challenging environments out there today and cutting-edge techniques to solve these environments
  • Foundational reinforcement learning concepts and methods
  • The most popular deep reinforcement learning agents solving high-dimensional environments
  • Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence

Course breakdown / modules

  • What is deep reinforcement learning?
  • The past, present, and future of deep reinforcement learning
  • The suitability of deep reinforcement learning
  • Setting clear two-way expectations

  • Components of reinforcement learning
  • MDPs: The engine of the environment

  • The objective of a decision-making agent
  • Planning optimal sequences of actions

  • The challenge of interpreting evaluative feedback
  • Strategic exploration

  • Learning to estimate the value of policies
  • Learning to estimate from multiple steps

  • The anatomy of reinforcement learning agents
  • Learning to improve policies of behavior
  • Decoupling behavior from learning

  • Learning to improve policies using robust targets
  • Agents that interact, learn and plan

  • The kind of feedback deep reinforcement learning agents use
  • Introduction to function approximation for reinforcement learning
  • NFQ: The first attempt to value-based deep reinforcement learning

  • DQN: Making reinforcement learning more like supervised learning
  • Using experience replay
  • Double DQN: Mitigating the overestimation of action-value functions

  • Dueling DDQN: A reinforcement-learning-aware neural network architecture
  • network brings to the table?
  • PER: Prioritizing the replay of meaningful experiences

  • REINFORCE: Outcome-based policy learning
  • VPG: Learning a value function
  • A3C: Parallel policy updates
  • GAE: Robust advantage estimation
  • A2C: Synchronous policy updates

  • DDPG: Approximating a deterministic policy
  • TD3: State-of-the-art improvements over DDPG
  • SAC: Maximizing the expected return and entropy

  • What was covered, and what notably was not?
  • More advanced concepts towards AGI
  • Fairness, and Ethical Standards
  • What happens next?

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