Feedback
Hey Adnan,
Awesome wiki page! It reads like a textbook (smooth and explains very well).
Some feedback:
- The multi-armed bandit problem is not defined but mentioned briefly when talking about exploration vs. exploitation, so it feels kinda out of place.
- The Bellman equation is references before it is defined.
- The example of a RL agent playing chess can be expanded a bit. What is the state-space and action space? How are the reward functions defined, etc? Essentially, how does this RL agent fit into the framework described in this wiki page?
- The "The Standard Reinforcement Learning Model" assumes discrete state-space, finite number of actions, and discrete time. Maybe a brief mention on whether these constraints can be relaxed and/or why it is difficult to do so.
Overall, amazing read!
Ricky
- [5] The topic is relevant for the course.
- [5] The writing is clear and the English is good.
- [5] The page is written at an appropriate level for CPSC 522 students (where the students have diverse backgrounds).
- [5] The formalism (definitions, mathematics) was well chosen to make the page easier to understand.
- [5] The abstract is a concise and clear summary.
- [3] There were appropriate (original) examples that helped make the topic clear.
- [3] There was appropriate use of (pseudo-) code.
- [5] It had a good coverage of representations, semantics, inference and learning (as appropriate for the topic).
- [5] It is correct.
- [4] It was neither too short nor too long for the topic.
- [5] It was an appropriate unit for a page (it shouldn't be split into different topics or merged with another page).
- [5] It links to appropriate other pages in the wiki.
- [5] The references and links to external pages are well chosen.
- [4.5] I would recommend this page to someone who wanted to find out about the topic.
- [4] This page should be highlighted as an exemplary page for others to emulate.