1d:T7bd,# qlearningaction This repository implements [Q-Learning](http://artint.info/html/ArtInt_265.html), a model-free form of reinforcement learning in R. ```r qlearningaction(q, currentstate, exploration=.5) ``` ## Arguments - `q`: Input state/action matrix. - `currentstate`: Current state of the game. Does not have to match any of the state for **q**. - `exploration`: The probability of choosing a random state, rather than the one with the highest EV. Default 0.5. ## Details For internal use for **qlearn**. ## Returns An action to take, taken from the possible actions of **q**. ## References http://labressler.github.io/analytics ## Author(s) Liam Bressler ## Note Contact at liam.bressler@yale.edu ## Examples ```r cardgame <- function() { playercards <- sample(1:8,4) #distribute the cards, we're player one ourcard <- playercards[1] #our card playertotals <- rep(-1,4) #including the antes playersinpot <- vector() for (player in 2:4) #other 3 players go first { if (playercards[player]>=2) { playertotals[player] <- (-3) playersinpot <- append(playersinpot,player) } } #the next line is where we want to choose our action player1 <- 'Choose' if (player1=="Call") { playertotals[1] <- (-3) playersinpot <- append(playersinpot,1) } potsize <- -1*(sum(playertotals)) #the amount in the pot is how much the players put in playercards[!(1:4 %in% playersinpot)] <- 0 #get rid of everyone who folded winner <- which.max(playercards) #winner is the person with the highest card who didn't fold playertotals[winner] <- playertotals[winner]+potsize return(playertotals[1]) #return how much we won } strat <- qlearn(game="cardgame",statevars="ourcard",possibleactions=c("Call","Fold"), playername="player1",numiter=25000) #make sure each function and variable name is a string qlearningaction(strat,3,exploration=.75) #Pick an action to perform when we have the 3 card, with high exploration ```

qlearningaction function