Dynamic programming and reinforcement learning

The details
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Undergraduate: Level 6
Monday 13 January 2025
Friday 21 March 2025
08 January 2024


Requisites for this module



Key module for


Module description

Machine learning has become a prominent tool in data analytics. One major category of it: the reinforcement learning, has been widely used in industry to maximise the notion of cumulative reward. This module is concerned with the conceptual background of reinforcement learning, i.e. Markov decision process (MDP) and dynamic programming.

Reinforcement learning, has been covered under Dynamic Programming for decades, designed on the divide-and-conquer basis, provide the structure to all the methods that have been developed in recent years. Describing the problem status by stages, states and transition matrices, but allowing decisions in the whole process.

Module aims

The aims of this module are:

  • To teach the basic mathematical concepts behind dynamic programming and reinforcement learning.

  • To link the solution approach with statistics, computing, simulation, calculus and standard optimization techniques.

Module learning outcomes

By the end of this module, students will be expected to have:

  1. Conceptual understanding of the “divide and conquer” idea behind dynamic programming and be able to construct deterministic and stochastic dynamic programming models.

  2. Detailed and systematic knowledge of key elements of a dynamic programming system and be able to write Bellman’s equations.

  3. Understand the basic techniques of backward induction and be able to write pseudo codes to implement on small scale problems.

  4. An ability to identify suitable approaches/software packages to solve some typical applications.

Module information

To use dynamic programming we require to have full knowledge of the dynamics of the problem (for example, knowing the probability that an autonomous car will not crash if it turns right at any point in time). These assumptions rarely hold in real-life problems, and to tackle those issues we will explore other methods that do not need such information, and just consider agents that learn by experience (trial & error).

We will also learn about modern reinforcement learning approaches and typical applications, that are designed for large scale problems and have shown excellent results in recent years. A very good example is the algorithm developed by DeepMind based on Deep Reinforcement Learning and Monte Carlo Tree Search that beat professional Go players.

The theory behind these problems and all the required concepts will be discussed in the Lectures, and complemented with ad-hoc experiments in the Laboratory Sessions.

Indicative syllabus

  • Basics of sequential decision process: stage, state, action, objective, policy, etc.

  • Deterministic dynamic Programming and Bellman's Equation.

  • Stochastic: Markov Decision Process (MDP).

  • Standard solution approach – backward induction and its computational challenge.

  • Approximation approaches – aggregation, regression, neural networks, etc.

  • Reinforcement learning techniques: Monte Carlo Methods, Q-learning, Temporal difference methods.

  • Applications: Knapsack problem, Multi-armed Bandit, Revenue Management, Vehicle Routing etc.

Learning and teaching methods

Teaching in the School will be delivered using a range of face to face lectures, classes and lab sessions as appropriate for each module. Modules may also include online only sessions where it is advantageous, for example for pedagogical reasons, to do so.


The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Lab report    33.3% 
Coursework   Project    66.7% 
Exam  Main exam: In-Person, Open Book (Restricted), 120 minutes during Summer (Main Period) 
Exam  Reassessment Main exam: In-Person, Open Book (Restricted), 120 minutes during September (Reassessment Period) 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
30% 70%


Coursework Exam
30% 70%
Module supervisor and teaching staff
Dr Felipe Maldonado, email:
Dr Felipe Maldonado



External examiner

Dr Yinghui Wei
University of Plymouth
Available via Moodle
Of 13 hours, 13 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.


Further information

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