Business↗
Approximate Value Functions and Online Planning
A handbook for solving large sequential decision problems using value-function approximation, receding-horizon control, rollouts, search, sparse sampling and Monte Carlo tree se…
Every page in the KEVOS library tagged online planning. 4 pages.
A handbook for solving large sequential decision problems using value-function approximation, receding-horizon control, rollouts, search, sparse sampling and Monte Carlo tree se…
A handbook for planning when system state is uncertain, including belief-state value functions, offline and online POMDP planning, point-based methods and controller abstractions.
Deciding from the current belief at run time by looking ahead over actions and observations — POMCP and belief-lookahead search for larger partially observable problems.
Deciding on the fly by looking ahead from the current state — forward search and Monte Carlo tree search — instead of precomputing a full policy.