---
title: "HybPlan"
source: https://www.jemoka.com/posts/kbhhybplan/
---

&ldquo;Can we come up a policy that, if not fast, at least reach the goal!&rdquo;
Background Stochastic Shortest-Path we are at an initial state, and we have a series of goal states, and we want to reach to the goal states.
We can solve this just by:
value iteration simulate a trajectory and only updating reachable state: RTDP, LRTDP MBP Problem MDP + Goal States
\(S\): set of states \(A\): actions \(P(s&rsquo;|s,a)\): transition \(C\): reward \(G\): absorbing goal states Approach Combining LRTDP with anytime dynamics
run GPT (not the transformer, &ldquo;General Planning Tool&rdquo;, think LRTDP) exact solver use GPT policy for solved states or visited more than a certain threshold uses MBP policy for other states policy evaluation for convergence &ldquo;use GPT solution as much as possible, and when we haven&rsquo;t ever visited a place due to the search trajectories, we can use MBP to supplement the solution&rdquo;
