# inst/examples/example_fish.R In boettiger-lab/pomdpplus: Planning and Learning in Uncertain Systems

```states <- 0:10
actions <- states
reward_fn <- function(x,h) pmin(x,h)
discount <- 0.95

sigma_g <- sqrt(log(1 + 0.5 / 6))
sigma_m <- sigma_g
f <- function(x, h, r = 1, K = 20){
s <- pmax(x - h, 0)
s * exp(r * (1 - s / K) )
}

kk = c(4,8); rr = c(0.5,1)
Num_model = length(kk) * length(rr)
T = vector("list", length = Num_model)
O = vector("list", length = Num_model)
n_s <- length(states)
n_a <- length(actions)
observed_states <- states
n_z <- length(observed_states)

observation <- array(0, dim = c(n_s, n_z, n_a))
for (k in 1:n_a) {
if(sigma_m <= 0){
observation[, , k] <- diag(n_s)
} else {
for (i in 1:n_s) {
if(states[i] <= 0){ ## cannot do dlnorm with mu = log(0) = -Inf.  Cannot solve if belief has already converged
x <- dlnorm(observed_states, -1, sigma_m)
observation[i, , k] <- x / sum(x)
} else {
x <- dlnorm(observed_states, log(states[i]), sdlog = sigma_m)    # transition probability densities
## Normalize using CDF
N <- plnorm(observed_states[n_s], log(states[i]), sigma_m)       # CDF accounts for prob density beyond boundary
x <- x * N / sum(x)                                   # normalize densities to  = cdf(boundary)
x[n_s] <- 1 - N + x[n_s]                              # pile remaining probability on boundary
observation[i, , k] <- x                             # store as row of transition matrix
}
}
}
}

kkk = 1
for(l in 1:length(kk)){
for(j in 1:length(rr)){
transition <- array(0, dim = c(n_s, n_s, n_a))
for (k in 1:n_s) {
for (i in 1:n_a) {
nextpop <- f(states[k], actions[i],rr[j],kk[l])
if(nextpop <= 0)
transition[k, , i] <- c(1, rep(0, n_s - 1))
else if(sigma_g > 0){
x <- dlnorm(states, log(nextpop), sdlog = sigma_g)    # transition probability densities
N <- plnorm(states[n_s], log(nextpop), sigma_g)       # CDF accounts for prob density beyond boundary
x <- x * N / sum(x)                                   # normalize densities to  = cdf(boundary)
x[n_s] <- 1 - N + x[n_s]                              # pile remaining probability on boundary
transition[k, , i] <- x                             # store as row of transition matrix
} else {
stop("sigma_g not > 0")
}
}
}
T[[kkk]] = transition; O[[kkk]] = observation
kkk = kkk+1
}
}

reward <- array(0, dim = c(n_s, n_a))
for (k in 1:n_s) {
for (i in 1:n_a) {
reward[k, i] <- reward_fn(states[k], actions[i])
}
}

t = 10
Num_sim = 10
n_true = 3
n_sample = 2
initial = array(1, dim = n_s) / n_s
P = (array(1,dim = length(T))/ length(T))
```
boettiger-lab/pomdpplus documentation built on Jan. 29, 2018, 3:59 a.m.