so have thought about some implementation strategies in relation with reinforcement learning end detected some basic problems: a) i need a way to detect the end of a game b) to rate it in that way that i can say which play has won this to statements are most importend to get known couse without this a implementation of a client could get very difficult, to determine life, death groups would be useful if i want to know how won a game so maybe the first step shoulb be develop a system that can manage the group problem the difference from go to chess or backgammon is u have no deterministic end of game and u dont know such easy who won it chess: the king backgamon: all stones at home go: ???? couse the structure of a ri-agent who should learn play a game - would be every move gets a reinforcement of r=0 if won the game r=1 if lost the game r=-1 the system than have itself to figure out why it lost or won, and figure out/aproximate a policy of how to play this game in a way to win it u tell him nothing about how to play, just win is good, lost is bad of course it have to be implemented the rules of the game ... of course a second possible structure could be: u have a multi agent like this: - detect groups (- rate groups) - loss of a group ==> negative reinforcement maybe r=-0.2 - catch a opponents group ==> positive reinforcement maybe r=+0.1 - save an area ... and so on and every agent only tries to maximies his reinforcement than is a super-agent - one how manage how importend which agent in wich siutation will be most importend but thats all future if i don't get sure statements about a) and b) ives
Attachment:
smime.p7s
Description: application/pkcs7-signature