The projects for AI
Thanks for using reinforcement learning to solve PD world !
Contact XiaoyangLi (xiaoyang.rebecca.li@gmail.com)
- Collaborators: Priyal Kulkarni ,Sarthak Sharma
%%%%%%%%%%%%%%%%%%%% Q_learning %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Language : C++
Program compiler : GNU GCC (IDE-codeblocks) http://www.codeblocks.org/downloads
[ output.txt ]
Header line: The experiment name, the numbered execution (each experiment is executed twice), the seed used for this execution.
#Subsequent lines are the q-tables:
- One line indicating step number, Reward and Blocks Delivered after every 40 steps
- The q-table using state representation 2: (i, j, x), ordered as: N S E W after first 100 steps, after 1st dropofff is filled and after each termination.
[Q_learning results]
Exactly the same content as Output.txt except we put all the result in separate files and 6 folders
%%%%%%%%%%%%%%%%%%%% Visualization %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Language : Matlab
[QtableFullstate . m] is the main function to generate screen shot of Q table and Fullstate
Input template: (We need to copy the Q table from txt file to the corresponding templates)
Qread0.dat for Q table x=0
Qread1.dat for Q table x=1
Output:
original fig images in [Visualization \ QtableFullstate Fig] folder
compacted jpg images in [QtableFullstate JPEG] folder
[PerformanceMeasure.m] is the main function to generate performance measurement
Input template: (We need to copy the Q table from txt file to the corresponding templates)
Perf.xlsx (variablenames = { 'steps','Reward','BlocksDelivered','BankAccount','Note'}
Output :
original fig images in [Visualization \ PerformanceMeasure Fig] folder
compacted images in appendix chaps of report.