Searching for the optimal Gimkit strategy
.github/ISSUE_TEMPLATE | ||
dp_solver.cpp | ||
README.md |
Gimkit
Ever wondered about what the optimal Gimkit strategy is? Well, now you can get a definite answer with dp_solver.cpp
, which uses a Dynamic Programming algorithm to search for the optimal Gimkit strategy. This algorithm can calculate the optimal strategy from any arbitary starting state.
How to use
// Initial conditions
// Change these values to alter the initial state
ll start = 0, goal = 1e10;
int max_it = 150; // Number of iterations to solve
int MPQ = 0, SB = 0, M = 0; // Initial upgrade status
int D = 0, R = 0, B1 = 0, B2 = 0; // Initial power-up status
Change these values to alter the initial state:
- start - Starting amount of money
- goal - Ending amount of money
- max_it - The maximum number of iterations to solve
- MPQ - The initial level of the money per question upgrade (Starts from 0)
- SB - The initial level of the streak bonus upgrade (Starts from 0)
- M - The initial level of the multiplier upgrade (Starts from 0)
- D - The status of the discounter power-up (0 is unpurchased, 1 is purchased and used)
- R - The status of the rebooter power-up (0 is unpurchased, 1 is purchased and used)
- B1 - The status of the mini bonus power-up (0 is unpurchased, 1 is purchased and unused, 2 is used)
- B2 - The status of the mega bonus power-up (0 is unpurchased, 1 is purchased and unused, 2 is used)
How it works
Coming soon