Imperial College London

DrEdwardJohns

Faculty of EngineeringDepartment of Computing

Lecturer
 
 
 
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Contact

 

e.johns Website

 
 
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Location

 

365ACE ExtensionSouth Kensington Campus

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Summary

 

Reinforcement Learning - COMP97143

Aims

The course provides both basic and advanced knowledge in reinforcement learning across three core skills: theory, implementation, and evaluation. Students will learn the fundamentals of both tabular reinforcement learning and deep reinforcement learning, and will gain experience in designing and implementing these methods for practical applications.

Specifically, students will:

  • Learn the theoretical foundations of reinforcement learning (Markov decision processes & dynamic programming).
  • Learn the algorithmic foundations of reinforcement learning (temporal difference and Monte-Carlo learning).
  • Gain experience in framing low-dimensional problems and implementing solutions using tabular reinforcement learning.
  • Learn about the motivation behind deep reinforcement learning and its relevance to high-dimensional applications, such as playing video games, and robotics.
  • Discover the state-of-the-art deep reinforcement learning algorithms such as Deep Q Networks (DQN), Proximal Policy Optimisation (PPO), and Soft Actor Critic (SAC).
  • Implement and experiment with a range of different deep reinforcement learning algorithms in Python and PyTorch, and learn how to visualise and evaluate their performance.

Role

Course Leader

Reinforcement Learning - COMP97144

Aims

The course provides both basic and advanced knowledge in reinforcement learning across three core skills: theory, implementation, and evaluation. Students will learn the fundamentals of both tabular reinforcement learning and deep reinforcement learning, and will gain experience in designing and implementing these methods for practical applications.

Specifically, students will:

  • Learn the theoretical foundations of reinforcement learning (Markov decision processes & dynamic programming).
  • Learn the algorithmic foundations of reinforcement learning (temporal difference and Monte-Carlo learning).
  • Gain experience in framing low-dimensional problems and implementing solutions using tabular reinforcement learning.
  • Learn about the motivation behind deep reinforcement learning and its relevance to high-dimensional applications, such as playing video games, and robotics.
  • Discover the state-of-the-art deep reinforcement learning algorithms such as Deep Q Networks (DQN), Proximal Policy Optimisation (PPO), and Soft Actor Critic (SAC).
  • Implement and experiment with a range of different deep reinforcement learning algorithms in Python and PyTorch, and learn how to visualise and evaluate their performance.

Role

Course Leader