如何在R程序中获取gridworld模型的SARSA代码？

我的研究案例有个问题。 我对gridworld模型的强化学习很感兴趣。 模型是由7x7个运动场组成的迷宫。 想想迷宫般的田野。有四个方向：上、下、左、右（或N、E、S、W）。因此，最多有一些政策。许多人在使用撞墙时立即给予的惩罚时可以被排除在外。 除此之外，还采用了返回抑制原则，通常允许的行动更少。许多政策仅在目标后的部分不同，或者是等效的

▼ 国家：有障碍 ▼ 奖励：如果r=1，如果s=G，则任何允许的移动r=0，否则r=-100 ▼ 初始化：Q0（a，s）~N（0,0.01）

为了解决这个模型，我做了一个R代码，但它不能正常工作

型号：7x7，S:开始状态，G:终端状态，O:可访问状态，X:墙

 [O,O,G,X,O,O,S]
 [O,X,O,X,O,X,X]
 [O,X,O,X,O,O,O]
 [O,X,O,X,O,X,O]
 [O,X,O,O,O,X,O]
 [O,X,O,X,O,X,O]
 [O,O,O,X,O,O,O]

所以我想知道如何更正这个gridworld模型的代码（不是uppon代码），并想知道如何通过SARSA模型求解这个模型

actions <- c("N", "S", "E", "W")

x <- 1:7
y <- 1:7

rewards <- matrix(rep(0, 49), nrow=7)

 rewards[1, 1] <- 0
 rewards[1, 2] <- 0
 rewards[1, 3] <- 1
 rewards[1, 4] <- -100
 rewards[1, 5] <- 0
 rewards[1, 6] <- 0
 rewards[1, 7] <- 0
 rewards[2, 1] <- 0
 rewards[2, 2] <- -100
 rewards[2, 3] <- 0
 rewards[2, 4] <- -100
 rewards[2, 5] <- 0
 rewards[2, 6] <- -100
 rewards[2, 7] <- -100
 rewards[3, 1] <- 0
 rewards[3, 2] <- -100
 rewards[3, 3] <- 0
 rewards[3, 4] <- -100
 rewards[3, 5] <- 0
 rewards[3, 6] <- 0
 rewards[3, 7] <- 0
 rewards[4, 1] <- 0
 rewards[4, 2] <- -100
 rewards[4, 3] <- 0
 rewards[4, 4] <- -100
 rewards[4, 5] <- 0
 rewards[4, 6] <- -100
 rewards[4, 7] <- 0
 rewards[5, 1] <- 0
 rewards[5, 2] <- -100
 rewards[5, 3] <- 0
 rewards[5, 4] <- 0
 rewards[5, 5] <- 0
 rewards[5, 6] <- -100
 rewards[5, 7] <- 0
 rewards[6, 1] <- 0
 rewards[6, 2] <- -100
 rewards[6, 3] <- 0
 rewards[6, 4] <- -100
 rewards[6, 5] <- 0
 rewards[6, 6] <- -100
 rewards[6, 7] <- 0
 rewards[7, 1] <- 0
 rewards[7, 2] <- 0
 rewards[7, 3] <- 0
 rewards[7, 4] <- -100
 rewards[7, 5] <- 0
 rewards[7, 6] <- 0
 rewards[7, 7] <- 0

 values <- rewards # initial values

 states <- expand.grid(x=x, y=y)

 # Transition probability
 transition <- list("N" = c("N" = 0.8, "S" = 0, "E" = 0.1, "W" = 0.1), 
         "S"= c("S" = 0.8, "N" = 0, "E" = 0.1, "W" = 0.1),
         "E"= c("E" = 0.8, "W" = 0, "S" = 0.1, "N" = 0.1),
         "W"= c("W" = 0.8, "E" = 0, "S" = 0.1, "N" = 0.1))

 # The value of an action (e.g. move north means y + 1)
 action.values <- list("N" = c("x" = 0, "y" = 1), 
         "S" = c("x" = 0, "y" = -1),
         "E" = c("x" = 1, "y" = 0),
         "W" = c("x" = -1, "y" = 0))

 # act() function serves to move the robot through states based on an action
 act <- function(action, state) {
     action.value <- action.values[[action]]
     new.state <- state
         if(state["x"] == 1 && state["y"] == 7 || (state["x"] == 1 && state["y"] == 3))
         return(state)
     #
     new.x = state["x"] + action.value["x"]
     new.y = state["y"] + action.value["y"]
     # Constrained by edge of grid
     new.state["x"] <- min(x[length(x)], max(x[1], new.x))
     new.state["y"] <- min(y[length(y)], max(y[1], new.y))
     #
     if(is.na(rewards[new.state["y"], new.state["x"]]))
         new.state <- state
     #
     return(new.state)
 }


 rewards

 bellman.update <- function(action, state, values, gamma=1) {
     state.transition.prob <- transition[[action]]
     q <- rep(0, length(state.transition.prob))
     for(i in 1:length(state.transition.prob)) {        
         new.state <- act(names(state.transition.prob)[i], state) 
         q[i] <- (state.transition.prob[i] * (rewards[state["y"],        state["x"]] + (gamma * values[new.state["y"], new.state["x"]])))
     }
     sum(q)
 }

 value.iteration <- function(states, actions, rewards, values, gamma, niter,      n) {
     for (j in 1:niter) {
         for (i in 1:nrow(states)) {
             state <- unlist(states[i,])
             if(i %in% c(7, 15)) next # terminal states
             q.values <- as.numeric(lapply(actions, bellman.update,      state=state, values=values, gamma=gamma))
             values[state["y"], state["x"]] <- max(q.values)
         }
     }
     return(values)
 }

 final.values <- value.iteration(states=states, actions=actions,      rewards=rewards, values=values, gamma=0.99, niter=100, n=10)

 final.values

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行动问题是你的惩罚远远大于奖励。代理人可能更愿意把自己扔进墙里，而不是试图得到报酬。这是因为状态动作值收敛到非常低的实数，甚至低于-100，这取决于动作的奖励

下面是我制作的一个模拟值迭代的模型（它显示了SARSA应该收敛到的值）：


值表表示图片中模型的值状态，但它是反向的（因为我还没有修复它）

在本例中，我将奖惩的价值与您的模型非常相似-15是一个不偏不倚的状态（一堵墙），1.0是球，-100是方块。代理对每个动作都得到0.0，并且转移概率也相同

代理必须到达球，但正如您所看到的，状态收敛到非常小的值。在这里，您可以看到球所在的相邻状态的值较低。因此，代理人宁愿永远也达不到自己的目标

为了解决你的问题，尽量减少惩罚