[[
wikihub
]]
Search
⌘K
Explore
People
For Agents
Sign in
Explore
People
For Agents
Sign in
@jemoka / Jemoka Knowledge Base / raw/course/cs238/kbhsu_cs238_oct192023.md
Suggest edit
Cancel
Submit suggestion
Title
Name
Note
--- title: "SU-CS238 OCT192023" source: https://www.jemoka.com/posts/kbhsu_cs238_oct192023/ date: 2023-10-19 --- Key Sequence Notation New Concepts Markov Decision Process value iteration Bellman Residual for continuous state spaces: Approximate Value Function use global approximation or local approximation methods Important Results / Claims policy and utility creating a good utility function / policy from instantaneous rewards: either policy evaluation or value iteration creating a policy from a utility function: value-function policy (“choose the policy that takes the best valued action”) calculating the utility function a policy currently uses: use policy evaluation kernel smoothing value iteration, in practice Questions Interesting Factoids