Avi's Notes

Final Year Project

4 min read

Links - Final Year Project

Timeline/Tasks

22 Jul 25 - Offline Meeting

  1. Research RL agent based approach
  2. Do literature survey and find what to optimize

20 Aug 25 - Online Meeting

  1. Chemotherapy dosing - scheduling - papers to be give by Ma’am
  2. need to prepare objective PPT on the methodology we will follow

17 Oct 25 - Offline Meeting

  1. Prepare PPT on entire project idea starting from ground up to final idea

28 Oct 25 - Offline Meeting

  1. Green light with initial PPT
  2. Proceed with rough literature survey with around 6 papers

19 Nov 25 - Offline Reporting to HOD

5th Dec 2025 - Online Meeting

  1. introduction - overview, current work then propose next work - 1.5 page
  2. literature survey - write about specific paper and work (include references) 6-7 para, 2.5-3 pages
  3. proposed work - limitations from literature, proposal, technologies, their descriptions, upto from current planning, proposed model - flow diagram, steps, make it look good, 3-4 pages
  4. future work - conclusion
  5. references - 10-12 papers
  6. in total - 20 pages, each section 3-4 pages
  7. presentation - 19/20 Dec - 15-16 pages
  8. Difference between research gaps and future work - what and what not to include???

14th Dec 2025 - Online Meeting

  1. Instructions on how to fix synopsis report + make PPT for final day
  2. General
    1. use AI to get gist and then paraphrase
    2. ETA of full project january-february
    3. no italics, no boldings
    4. 11pts
    5. Times New Roman
    6. college visit either on 16th or 18th 1st half
    7. only one can visit to sign
    8. questions based on how extensively study has been done not how much work has been done
    9. 4 panel members
    10. TOC shouldn’t include Abstract, Acknowledgement, Approval
    11. 10-15 citations - in proper order, should start with 1 → 2 → 3 → 4 …
  3. Questions in panel
    1. How RL is used? - proposed algorithms
    2. Why RL is used?
    3. How RL works?
    4. Explanation of MDP - action, state, …
    5. relation to traffic control systems
    6. detail of algorithms in proposed work - how and why
  4. Acknowledgement
    1. grammatical mistakes their
  5. Abstract
    1. 150-200 words
    2. intent of work, challenges
    3. don’t include limitations
    4. in 1-2 lines
      1. Traffic Management System → why RL → why MARL
    5. focus should be on traffic signal - to fix problems with traffic congestion
  6. Introduction
    1. no proposed methodology, overview, background section
    2. in normal paras
    3. no bullet points
    4. overall work description
      1. Traffic → RL → MARL implementation → What gaps and proposals
    5. What, Why, How?
    6. 2 pages
  7. Literature survey
    1. info obtained from other people’s work
    2. MDP in detail not needed - maybe just “To apply RL … MDP needed”
    3. don’t include self proposed figures (include in proposed methodology section)
    4. don’t include formulas
    5. RL, MDP, MARL, traffic signal/control systems, relation of RL with traffic signal elaborate descriptions FIX!
    6. What gaps are there in traffic signal optimization by virtue of which we are applying MARL?
    7. limitations in traffic control systems
    8. algorithms used in different papers by other people
    9. don’t make headings - in paras
  8. Proposed work
    1. can use bold for MDP sub fields
    2. include proposed figures here (of solutions, methods)
    3. don’t include monetary facts
    4. discuss drawbacks → easing into MARL solutions
    5. MDP, MARL extensive description
    6. road map
    7. must cover extensively
    8. mention all of the research gaps - including future work
      1. mention specifically which gap we are intending to work on
      2. exact algorithm and road map of gaps
    9. proposed algorithms
      1. like CLRS algorithms - 12-14 lines
      2. flowcharts
      3. how exactly to renovate the present conditions
      4. just proposed works - step wise not pseudo code
      5. include after implementation roadmap
  9. Conclusion
    1. don’t give separate heading for upcoming research phase
  10. PPT structure
    1. 10-15 pages (max 15)
    2. in bullet points
      1. which problem was highlighted
      2. what is MDP, RL
      3. contribution of RL in the problem
      4. what is MARL
      5. limitations of MARL in traffic congestion system
    3. add pictures
    4. proposed work
      1. planning/road map
      2. which gaps were found out
      3. explain using images
      4. proposed algorithm
      5. proposed methodology
    5. conclusion - MARL

Project/Thesis Topic Suggestion

  1. https://thesis.cs.ut.ee/
  2. https://topics.cs.ut.ee/
  3. https://www.reddit.com/r/cscareerquestionsEU/comments/jstxgd/comment/gc1jp3g/
  4. https://docs.google.com/document/d/11WOBZKXOIwo0JbQCQaqzZCFBB8ho2Hn-BhA2wwnh1N4/edit?tab=t.0
  5. https://cs230.stanford.edu/past-projects/
  6. https://web.stanford.edu/class/cs224n/project.html
  7. https://www.cs.ox.ac.uk/teaching/courses/projects/
  8. https://www.cst.cam.ac.uk/teaching/part-ii/projects/project-suggestions
  9. https://scholarworks.lib.csusb.edu/computersci-engineering-etd/

Miscellaneous

  1. https://github.com/papers-we-love/papers-we-love
  2. https://lmcinnes.github.io/datamapplot_examples/ArXiv_data_map_example.html
  3. https://cs229.stanford.edu/suggestions.html - Dead link

Potential Topics

  1. AI based caching algorithm
  2. continuous intruder detection by learning usage patterns
  3. deep learning for physics discovery
  4. stroke detection
  5. CA → AI (financial; investments)
  6. https://rentry.org/finalYearML

Literature Study

  1. https://www.indiatoday.in/diu/story/traffic-is-choking-indian-cities-but-there-may-be-a-radical-solution-2759488-2025-07-22
  2. SCOOT
  3. SCATS
  4. Mnih, V. et al. (2015) ‘Human-Level Control Through Deep Reinforcement Learning’, Nature, 518: 529–533
  5. Hu, T.-Y., and Li, Z.-Y. (2024) ‘A Multi-Agent Deep Reinforcement Learning Approach for Traffic Signal Coordination’, IET Intelligent Transport Systems, 18: 1428–1444.
  6. Wei, H. et al. (2019) ‘Colight: Learning Network-Level Cooperation for Traffic Signal Control’, in International Conference on Information and Knowledge Management, pp. 1913–22. https://doi.org/10.1145/3357384.3357902.
  7. Zhang, Y. et al. (2024c) ‘Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning’, IEEE Transactions on Mobile Computing, 23: 7180–7195
  8. Presslight
PaperAnalysisRating
Advances in reinforcement learning for traffic signal
control: a review of recent progress		Good introductory analysis + Review of recent times4

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