Under construction. This area is a framework being built: most courses have no teaching material yet, and things may change or break. Private self-study. Not an official programme and not recognised by any ministry or school. No qualification, mark or document is issued, and “years 1 to 3” and “advanced” are names of study paths only.
E15 Reinforcement learning theory
Advanced pathE: Proposed extensionsplannedproposed, not in any programmeabout 160 h (planning estimate)
Planned: syllabus only, no teaching material yet. Nothing on this page can be studied; it describes what the course would cover.
In no programme. This is a proposed extension course; the owner has not approved it into a programme.
Proposed by the assistant to fill a gap in the owner's catalogue. It is in no programme until the owner approves it.
Why this course
Connects control, probability and learning.
Prerequisites
- Required
- A17 Stochastic & optimal control, G03 Graduate probability
- Recommended
- none
- Can be taken at the same time
- none
- Everything required, transitively (25 courses)
- A02, A05, A07, A17, B01, B02, B03, B04, B05, B06, B07, B08, B09, B11, B12, B13, B15, B19, B20, F01, F02, F03, G01, G03, U07
- Courses that require this one
- none
Concept-level units
Units follow the syllabus order, as a recommended sequence rather than a gate.
- Markov decision processes
- Dynamic programming and Bellman operators
- Temporal-difference learning
- Policy gradients
- Exploration and sample complexity
- Function approximation and its limits
Assessable outcome
Analyse an RL algorithm and state its convergence or sample-complexity result precisely.
Assessment design
- deeper problem sets
- reconstruction of an unfamiliar argument
- advanced source reading
- cumulative examination or substantial project
cumulative; uses spaced retrieval
Resources
Every title here is candidate, unverified unless marked verified. Titles were suggested while planning. Nobody has checked that they exist, what they cost or how they are licensed.
- Sutton and Barto · Reinforcement Learning (free)
- Agarwal, Jiang, Kakade and Sun · RL Theory and Algorithms (free)
Content record
- Version
- 0.0.0
- Review
- no review of any kind
- Provenance
- PROPOSED by the assistant on 30 Oct 2026 to fill gaps in the brief's catalogue; not in the owner's brief; needs the owner's approval and a curriculum reviewer
- Known issues
- Proposed course: not counted by any programme audit until the owner approves adding it to an elective pool.
- Type
- course
Bridges and laboratories
No bridge or laboratory is attached to this course yet.
You and this course
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