Imperial College London

Dr Paul A. Bilokon

Faculty of EngineeringDepartment of Computing

Lecturer in Mathematics for Computer Science
 
 
 
//

Contact

 

+44 (0)20 7594 8241paul.bilokon01 Website CV

 
 
//

Location

 

Huxley BuildingSouth Kensington Campus

//

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 - COMP70028

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

Calculus - COMP40016

Aims

In this module you will study the calculus needed for many applications of Computing, such as graphics, vision, robotics, operations research and statsitical machine learning. It also provides the background for follow-on mathematics modules that lay the foundations for advanced electives in the above topics and other mathematically-focused areas such as optimisation and finance.

Role

Course Leader