[譯] 強化學習入門篇：Simmer 模擬平臺高階使用技巧

HarryZhu發表於2019-07-20

如何與環境互動

在模擬過程中，許多 activity 是以函式的形式作為引數傳入的。這些函式可能與環境互動，比如now函式用來提取環境當前的時間，get_capacity 函式用於提取環境中resource對應的容量，get_n_generated函式用於獲取生成器的狀態，或者用 get_mon 函式直接收集的歷史監測值。唯一需要注意的是，模擬環境必須要包含在軌跡之中，下面是一個錯誤示例：

library(simmer)
library(simmer.plot)

t <- trajectory() %>%
  log_(function() as.character(now(env)))

env <- simmer() %>%
  add_generator("dummy", t, function() 1) %>%
  run(4)
#> 1: dummy0:
#> Error in now(env): object 'env' not found

因為，env 是全域性變數，它無法在執行時執行。模擬執行過程於模擬結果的賦值需要分開。在這個模擬用例中，環境 env 由軌跡 t 生成，可以通過 run()方法將整個過程分離開來：

t <- trajectory() %>%
  log_(function() as.character(now(env)))

env <- simmer() %>%
  add_generator("dummy", t, function() 1)

env %>% run(4) %>% invisible
#> 1: dummy0: 1
#> 2: dummy1: 2
#> 3: dummy2: 3

我們獲取了預期結果。但是，作為最佳實踐的通用規則，還是建議環境在最初單獨初始化，這樣可以避免不必要的錯誤，也使得程式碼更具有可讀性：

# 首先，初始化環境
env <- simmer()

# 生成軌跡
t <- trajectory() %>%
  log_(function() as.character(now(env)))

# 執行環境模擬過程
env %>%
  add_generator("dummy", t, function() 1) %>%
  run(4) %>% invisible
#> 1: dummy0: 1
#> 2: dummy1: 2
#> 3: dummy2: 3

行動集合

當生成器建立一個到達流的時候,它會給軌跡分配一個到達物件。軌跡在這裡的定義是由一個到達物件在系統中全生命週期的一系列行為。一旦一個到達物件被分配到軌跡中，它通常會以一定的順序開始執行軌跡中的預期行為，最後離開系統。比如:

patient_traj <- trajectory(name = "patient_trajectory") %>%
  seize(resource = "doctor", amount = 1) %>%
  timeout(task = 3) %>%
  release(resource = "doctor", amount = 1)

這裡我們建立一個病人就醫3分鐘然後離開的例子。這是一個直截了當的例子，但是大部分軌跡相關的函式都在此基礎上演化高階用法，下面會一一介紹。

此外, 建議你可以嘗試下simmer的外掛 simmer.bricks 包,它封裝了常用的一些軌跡。（見 simmer.bricks入門）

`log_()`

log_(., message, level) 方法用來列印模擬過程中的資訊以輔助debug，通過不同的 level 可以調整列印的層次:

t <- trajectory() %>%
  log_("this is always printed") %>% # level = 0 by default
  log_("this is printed if `log_level>=1`", level = 1) %>%
  log_("this is printed if `log_level>=2`", level = 2)

simmer() %>%
  add_generator("dummy", t, at(0)) %>%
  run() %>% invisible
#> 0: dummy0: this is always printed

simmer(log_level = 1) %>%
  add_generator("dummy", t, at(0)) %>%
  run() %>% invisible
#> 0: dummy0: this is always printed
#> 0: dummy0: this is printed if `log_level>=1`

simmer(log_level = Inf) %>%
  add_generator("dummy", t, at(0)) %>%
  run() %>% invisible
#> 0: dummy0: this is always printed
#> 0: dummy0: this is printed if `log_level>=1`
#> 0: dummy0: this is printed if `log_level>=2`

`set_attribute(), set_global()`

set_attribute(., keys, values) 方法提供了設定到達流屬性的方法。keys和values可以以向量或者函式的形式返回。但是， values只能夠以數值型表示。

patient_traj <- trajectory(name = "patient_trajectory") %>%
  set_attribute(keys = "my_key", values = 123) %>%
  timeout(5) %>%
  set_attribute(keys = "my_key", values = 456)

env <- simmer() %>%
  add_generator("patient", patient_traj, at(0), mon = 2) %>%
  run()

get_mon_attributes(env)
#>   time     name    key value replication
#> 1    0 patient0 my_key   123           1
#> 2    5 patient0 my_key   456           1

如上，軌跡的到達流在 0 時刻(通過 at 函式實現)，僅包含 {my_key:123} 的屬性。add_generator的引數 mon = 2表示對到達流的屬性進行持續觀察。我們可以用 get_mon_attributes 方法檢視 my_key 對應的值在模擬過程中的變化。

如果你想要設定一個存在依賴鏈路的屬性也是允許的。屬性可以通過get_attribute(., keys) 的方式獲取。下面是一個實際用例：

patient_traj <- trajectory(name = "patient_trajectory") %>%
  set_attribute("my_key", 123) %>%
  timeout(5) %>%
  set_attribute("my_key", 1, mod="+") %>%
  timeout(5) %>%
  set_attribute("dependent_key", function() ifelse(get_attribute(env, "my_key")<=123, 1, 0)) %>%
  timeout(5) %>%
  set_attribute("independent_key", function() runif(1))

env<- simmer() %>%
  add_generator("patient", patient_traj, at(0), mon = 2)
env %>% run()
#> simmer environment: anonymous | now: 15 | next: 
#> { Monitor: in memory }
#> { Source: patient | monitored: 2 | n_generated: 1 }

get_mon_attributes(env)
#>   time     name             key       value replication
#> 1    0 patient0          my_key 123.0000000           1
#> 2    5 patient0          my_key 124.0000000           1
#> 3   10 patient0   dependent_key   0.0000000           1
#> 4   15 patient0 independent_key   0.5500812           1

對於每一次到達，屬性只對於到達者可見，其餘人不可見。

writer <- trajectory() %>%
  set_attribute(keys = "my_key", values = 123)

reader <- trajectory() %>%
  log_(function() paste0(get_attribute(env, "my_key")))

env <- simmer() %>%
  add_generator("writer", writer, at(0), mon = 2) %>%
  add_generator("reader", reader, at(1), mon = 2)
env %>% run()
#> 1: reader0: NA
#> simmer environment: anonymous | now: 1 | next: 
#> { Monitor: in memory }
#> { Source: writer | monitored: 2 | n_generated: 1 }
#> { Source: reader | monitored: 2 | n_generated: 1 }

get_mon_attributes(env)
#>   time    name    key value replication
#> 1    0 writer0 my_key   123           1

因此，在前例中 reader 獲取的返回值是缺失值。不過，屬性也可以通過 set_global(., keys, values) 全域性變數宣告:

writer <- trajectory() %>%
  set_global(keys = "my_key", values = 123) 

reader <- trajectory() %>%
  log_(function() paste0(get_attribute(env, "my_key"), ", ", 
                         get_global(env, "my_key")))

env <- simmer() %>%
  add_generator("writer", writer, at(0), mon = 2) %>%
  add_generator("reader", reader, at(1), mon = 2)
env %>% run()
#> 1: reader0: NA, 123
#> simmer environment: anonymous | now: 1 | next: 
#> { Monitor: in memory }
#> { Source: writer | monitored: 2 | n_generated: 1 }
#> { Source: reader | monitored: 2 | n_generated: 1 }

get_mon_attributes(env)
#>   time name    key value replication
#> 1    0      my_key   123           1

如上顯示，全域性變數通過 get_mon_attributes() 賦值未命名的鍵值對。

`timeout(), timeout_from_attribute()`

timeout(., task) 通過給軌跡分配一定的時間來延遲使用者的到達行為，回顧之前最簡單的病人看病模型，通過賦予 task引數一個固定值實現超時機制。

patient_traj <- trajectory(name = "patient_trajectory") %>%
  timeout(task = 3)

env <- simmer() %>%
  add_generator("patient", patient_traj, at(0)) %>%
  run()

get_mon_arrivals(env)
#>       name start_time end_time activity_time finished replication
#> 1 patient0          0        3             3     TRUE           1

通常，超時是依賴於一個分佈假設或者通過屬性進行設定的，它通過給 task 引數傳入一個函式實現。

patient_traj <- trajectory(name = "patient_trajectory") %>%
  set_attribute("health", function() sample(20:80, 1)) %>%
  # distribution-based timeout
  timeout(function() rexp(1, 10)) %>%
  # attribute-dependent timeout
  timeout(function() (100 - get_attribute(env, "health")) * 2)

env <- simmer() %>%
  add_generator("patient", patient_traj, at(0), mon = 2)
env %>% run()
#> simmer environment: anonymous | now: 52.123429586641 | next: 
#> { Monitor: in memory }
#> { Source: patient | monitored: 2 | n_generated: 1 }

get_mon_arrivals(env)
#>       name start_time end_time activity_time finished replication
#> 1 patient0          0 52.12343      52.12343     TRUE           1
get_mon_attributes(env)
#>   time     name    key value replication
#> 1    0 patient0 health    74           1

如果想通過 timeout() 方法動態地設定 task引數，需要通過回撥函式的方式操作。比如 timeout(function() rexp(1, 10)),rexp(1, 10) 將被每次活動超時都執行。但是，如果你不通過回撥函式方式操作，它只會以靜態值的方式在初始化的時候執行一次，比如 timeout(rexp(1, 10))。

當然，通過回撥函式的方式會使得程式碼實現複雜功能，比如同時要檢查資源的狀態，和環境中其他實體互動等等。同樣地，對於其他活動型別，也都是可以以泛函的方式操作。

如果你只需要延遲設定屬性值那麼可以考慮 timeout_from_attribute(., key) 或者 timeout_from_global(., key), 因此，下面兩個個超時寫法是等價的，但是後者的顯然簡單很多。

traj <- trajectory() %>%
  set_attribute("delay", 2) %>%
  timeout(function() get_attribute(env, "delay")) %>%
  log_("first timeout") %>%
  timeout_from_attribute("delay") %>%
  log_("second timeout")

env <- simmer() %>%
  add_generator("dummy", traj, at(0))
env %>% run() %>% invisible
#> 2: dummy0: first timeout
#> 4: dummy0: second timeout

`seize(), release()`

seize(., resource, amount) 用於獲取指定數量的資源。相反地，release(., resource, amount) 用於釋放指定數量的資源。需要注意的是，為了使用這些函式來指定資源，你需要在模擬環境中通過 add_resource 函式來初始化。

patient_traj <- trajectory(name = "patient_trajectory") %>%
  seize(resource = "doctor", amount = 1) %>%
  timeout(3) %>%
  release(resource = "doctor", amount = 1)

env <- simmer() %>%
  add_resource("doctor", capacity=1, mon = 1) %>%
  add_generator("patient", patient_traj, at(0)) %>%
  run()

get_mon_resources(env)
#>   resource time server queue capacity queue_size system limit replication
#> 1   doctor    0      1     0        1        Inf      1   Inf           1
#> 2   doctor    3      0     0        1        Inf      0   Inf           1

這裡 add_resource() 中的引數 mon=1 表示模擬環境監控資源使用情況。使用 get_mon_resources(env) 可以獲取資源在模擬系統中的日誌流水。

有時候，資源的獲取和釋放希望通過依賴的到達流屬性進行動態調整。為了實現這個工恩呢該，你可以在 amount引數中傳入get_attribute(.)來代替之前的固定值。

patient_traj <- trajectory(name = "patient_trajectory") %>%
  set_attribute("health", function() sample(20:80, 1)) %>%
  set_attribute("docs_to_seize", function() ifelse(get_attribute(env, "health")<50, 1, 2)) %>%
  seize("doctor", function() get_attribute(env, "docs_to_seize")) %>%
  timeout(3) %>%
  release("doctor", function() get_attribute(env, "docs_to_seize"))
#> Warning in is.na(env[[name]]): is.na() applied to non-(list or vector) of
#> type 'closure'
#> Warning in is.na(amount): is.na() applied to non-(list or vector) of type
#> 'closure'

env <- simmer() %>%
  add_resource("doctor", capacity = 2, mon = 1) %>%
  add_generator("patient", patient_traj, at(0), mon = 2)
env %>% run()
#> simmer environment: anonymous | now: 3 | next: 
#> { Monitor: in memory }
#> { Resource: doctor | monitored: 1 | server status: 0(2) | queue status: 0(Inf) }
#> { Source: patient | monitored: 2 | n_generated: 1 }

get_mon_resources(env)
#>   resource time server queue capacity queue_size system limit replication
#> 1   doctor    0      2     0        2        Inf      2   Inf           1
#> 2   doctor    3      0     0        2        Inf      0   Inf           1
get_mon_attributes(env)
#>   time     name           key value replication
#> 1    0 patient0        health    80           1
#> 2    0 patient0 docs_to_seize     2           1

預設情況下，seize() 失敗會導致拒絕到達。下面的例子中，第二位病人嘗試找僅有的一名正在給另外一位病人看病的醫生看病，在沒有等候區的情況下就會發生拒絕。

patient_traj <- trajectory(name = "patient_trajectory") %>%
  log_("arriving...") %>%
  seize("doctor", 1) %>%
  # the second patient won't reach this point
  log_("doctor seized") %>%
  timeout(5) %>%
  release("doctor", 1)

env <- simmer() %>%
  add_resource("doctor", capacity = 1, queue_size = 0) %>%
  add_generator("patient", patient_traj, at(0, 1)) %>%
  run()
#> 0: patient0: arriving...
#> 0: patient0: doctor seized
#> 1: patient1: arriving...

get_mon_arrivals(env)
#>       name start_time end_time activity_time finished replication
#> 1 patient1          1        1             0    FALSE           1
#> 2 patient0          0        5             5     TRUE           1
get_mon_resources(env)
#>   resource time server queue capacity queue_size system limit replication
#> 1   doctor    0      1     0        1          0      1     1           1
#> 2   doctor    5      0     0        1          0      0     1           1

有時，你不想拒絕不成功的seize(),可以提供另外一條路徑。比如在例子中，我們改為第二名病人也可以先去看看護士：

patient_traj <- trajectory(name = "patient_trajectory") %>%
  log_("arriving...") %>%
  seize("doctor", 1, continue = FALSE,
        reject = trajectory("rejected patient") %>%
          log_("rejected!") %>%
          seize("nurse", 1) %>%
          log_("nurse seized") %>%
          timeout(2) %>%
          release("nurse", 1)) %>%
  # the second patient won't reach this point
  log_("doctor seized") %>%
  timeout(5) %>%
  release("doctor", 1)

env <- simmer() %>%
  add_resource("doctor", capacity = 1, queue_size = 0) %>%
  add_resource("nurse", capacity = 10, queue_size = 0) %>%
  add_generator("patient", patient_traj, at(0, 1)) %>%
  run()
#> 0: patient0: arriving...
#> 0: patient0: doctor seized
#> 1: patient1: arriving...
#> 1: patient1: rejected!
#> 1: patient1: nurse seized

get_mon_arrivals(env)
#>       name start_time end_time activity_time finished replication
#> 1 patient1          1        3             2     TRUE           1
#> 2 patient0          0        5             5     TRUE           1
get_mon_resources(env)
#>   resource time server queue capacity queue_size system limit replication
#> 1   doctor    0      1     0        1          0      1     1           1
#> 2    nurse    1      1     0       10          0      1    10           1
#> 3    nurse    3      0     0       10          0      0    10           1
#> 4   doctor    5      0     0        1          0      0     1           1

continue 標記意味著不論是否 reject發生，子軌跡都會緊跟著主軌跡執行。在這個例子中，continue=FALSE 意味著被拒絕的到達流獲取護士和釋放護士後就徹底結束了到達流的生命週期。否則，它將繼續在主軌跡中執行行動。

注意第二位病人可能也會持續嘗試，如果他執意想看這位醫生。

patient_traj <- trajectory(name = "patient_trajectory") %>%
  log_("arriving...") %>%
  seize("doctor", 1, continue = FALSE,
        reject = trajectory("rejected patient") %>%
          log_("rejected!") %>%
          # go for a walk and try again
          timeout(2) %>%
          log_("retrying...") %>%
          rollback(amount = 4, times = Inf)) %>%
  # the second patient will reach this point after a couple of walks
  log_("doctor seized") %>%
  timeout(5) %>%
  release("doctor", 1) %>%
  log_("leaving")

env <- simmer() %>%
  add_resource("doctor", capacity = 1, queue_size = 0) %>%
  add_generator("patient", patient_traj, at(0, 1)) %>%
  run()
#> 0: patient0: arriving...
#> 0: patient0: doctor seized
#> 1: patient1: arriving...
#> 1: patient1: rejected!
#> 3: patient1: retrying...
#> 3: patient1: rejected!
#> 5: patient1: retrying...
#> 5: patient0: leaving
#> 5: patient1: doctor seized
#> 10: patient1: leaving

get_mon_arrivals(env)
#>       name start_time end_time activity_time finished replication
#> 1 patient0          0        5             5     TRUE           1
#> 2 patient1          1       10             9     TRUE           1
get_mon_resources(env)
#>   resource time server queue capacity queue_size system limit replication
#> 1   doctor    0      1     0        1          0      1     1           1
#> 2   doctor    5      0     0        1          0      0     1           1
#> 3   doctor    5      1     0        1          0      1     1           1
#> 4   doctor   10      0     0        1          0      0     1           1

post.seize 是另一個可能的子軌跡選項，它在成功執行 seize() 後被執行。

patient_traj <- trajectory(name = "patient_trajectory") %>%
  log_("arriving...") %>%
  seize("doctor", 1, continue = c(TRUE, TRUE),
        post.seize = trajectory("admitted patient") %>%
          log_("admitted") %>%
          timeout(5) %>%
          release("doctor", 1),
        reject = trajectory("rejected patient") %>%
          log_("rejected!") %>%
          seize("nurse", 1) %>%
          timeout(2) %>%
          release("nurse", 1)) %>%
  # both patients will reach this point, as continue = c(TRUE, TRUE)
  timeout(10) %>%
  log_("leaving...")

env <- simmer() %>%
  add_resource("doctor", capacity = 1, queue_size = 0) %>%
  add_resource("nurse", capacity = 10, queue_size = 0) %>%
  add_generator("patient", patient_traj, at(0, 1)) %>%
  run()
#> 0: patient0: arriving...
#> 0: patient0: admitted
#> 1: patient1: arriving...
#> 1: patient1: rejected!
#> 13: patient1: leaving...
#> 15: patient0: leaving...

get_mon_arrivals(env)
#>       name start_time end_time activity_time finished replication
#> 1 patient1          1       13            12     TRUE           1
#> 2 patient0          0       15            15     TRUE           1
get_mon_resources(env)
#>   resource time server queue capacity queue_size system limit replication
#> 1   doctor    0      1     0        1          0      1     1           1
#> 2    nurse    1      1     0       10          0      1    10           1
#> 3    nurse    3      0     0       10          0      0    10           1
#> 4   doctor    5      0     0        1          0      0     1           1

`set_capacity(), set_queue_size()`

set_capacity(., resource, value) 可以設定資源容量，set_queue_size(., resource, value) 則可以設定佇列長度。注意，在使用這些函式之前，要記得在環境初始化時通過 add_resource 初始化資源，同樣這裡也支援靜態和動態兩種型別的賦值模式。

這些行為很有意思,它引入了動態變化的資源。例如,兩個軌跡爭取資源的能力:

set.seed(12345)

t1 <- trajectory() %>%
  seize("res1", 1) %>%
  set_capacity(resource = "res1", value = 1, mod="+") %>%
  set_capacity(resource = "res2", value = -1, mod="+") %>%
  timeout(function() rexp(1, 1)) %>%
  release("res1", 1)

t2 <- trajectory() %>%
  seize("res2", 1) %>%
  set_capacity(resource = "res2", value = 1, mod="+") %>%
  set_capacity(resource = "res1", value = -1, mod="+") %>%
  timeout(function() rexp(1, 1)) %>%
  release("res2", 1)

env <- simmer() %>%
  add_resource("res1", capacity = 20, queue_size = Inf) %>%
  add_resource("res2", capacity = 20, queue_size = Inf) %>%
  add_generator("t1_", t1, function() rexp(1, 1)) %>%
  add_generator("t2_", t2, function() rexp(1, 1)) %>%
  run(100)

plot(get_mon_resources(env), "usage", c("res1", "res2"), steps = TRUE)

`select()`

當資源在環境中事先分配時，seize(), release(), set_capacity() 和 set_queue_size() 可以順利使用，但有時候資源也需要通過一些策略動態選擇。比如下面的情況，select(., resources, policy, id)方法提供了選擇資源的一種方法，根據特定策略來選擇：
seize_selected(), release_selected(),set_capacity_selected(),set_queue_size_selected()。

patient_traj <- trajectory(name = "patient_trajectory") %>%
  select(resources = c("doctor1", "doctor2", "doctor3"), policy = "round-robin") %>%
  set_capacity_selected(1) %>%
  seize_selected(amount = 1) %>%
  timeout(5) %>%
  release_selected(amount = 1)

env <- simmer() %>%
  add_resource("doctor1", capacity = 0) %>%
  add_resource("doctor2", capacity = 0) %>%
  add_resource("doctor3", capacity = 0) %>%
  add_generator("patient", patient_traj, at(0, 1, 2)) %>%
  run()

get_mon_arrivals(env)
#>       name start_time end_time activity_time finished replication
#> 1 patient0          0        5             5     TRUE           1
#> 2 patient1          1        6             5     TRUE           1
#> 3 patient2          2        7             5     TRUE           1
get_mon_resources(env)
#>   resource time server queue capacity queue_size system limit replication
#> 1  doctor1    0      0     0        1        Inf      0   Inf           1
#> 2  doctor1    0      1     0        1        Inf      1   Inf           1
#> 3  doctor2    1      0     0        1        Inf      0   Inf           1
#> 4  doctor2    1      1     0        1        Inf      1   Inf           1
#> 5  doctor3    2      0     0        1        Inf      0   Inf           1
#> 6  doctor3    2      1     0        1        Inf      1   Inf           1
#> 7  doctor1    5      0     0        1        Inf      0   Inf           1
#> 8  doctor2    6      0     0        1        Inf      0   Inf           1
#> 9  doctor3    7      0     0        1        Inf      0   Inf           1

如果你提供給 select()提供一組動態的資源，那麼後續可以通過 seize_selected()調整獲取資源的策略。

patient_traj <- trajectory(name = "patient_trajectory") %>%
  set_attribute("resource", function() sample(1:3, 1)) %>%
  select(resources = function() paste0("doctor", get_attribute(env, "resource"))) %>%
  seize_selected(amount = 1) %>%
  timeout(5) %>%
  release_selected(amount = 1)

env <- simmer() %>%
  add_resource("doctor1", capacity = 1) %>%
  add_resource("doctor2", capacity = 1) %>%
  add_resource("doctor3", capacity = 1) %>%
  add_generator("patient", patient_traj, at(0, 1, 2), mon = 2)
env %>% run()
#> simmer environment: anonymous | now: 10 | next: 
#> { Monitor: in memory }
#> { Resource: doctor1 | monitored: TRUE | server status: 0(1) | queue status: 0(Inf) }
#> { Resource: doctor2 | monitored: TRUE | server status: 0(1) | queue status: 0(Inf) }
#> { Resource: doctor3 | monitored: TRUE | server status: 0(1) | queue status: 0(Inf) }
#> { Source: patient | monitored: 2 | n_generated: 3 }

get_mon_attributes(env)
#>   time     name      key value replication
#> 1    0 patient0 resource     3           1
#> 2    1 patient1 resource     3           1
#> 3    2 patient2 resource     2           1
get_mon_arrivals(env)
#>       name start_time end_time activity_time finished replication
#> 1 patient0          0        5             5     TRUE           1
#> 2 patient2          2        7             5     TRUE           1
#> 3 patient1          1       10             5     TRUE           1
get_mon_resources(env)
#>   resource time server queue capacity queue_size system limit replication
#> 1  doctor3    0      1     0        1        Inf      1   Inf           1
#> 2  doctor3    1      1     1        1        Inf      2   Inf           1
#> 3  doctor2    2      1     0        1        Inf      1   Inf           1
#> 4  doctor3    5      1     0        1        Inf      1   Inf           1
#> 5  doctor2    7      0     0        1        Inf      0   Inf           1
#> 6  doctor3   10      0     0        1        Inf      0   Inf           1

`activate(), deactivate()`

activate(., source)和deactivate(., source) 方法能夠分別按照ID來開始和暫停活動。這個名字可以提供一個字串或一個函式返回一個字串。在以下簡單的例子中,使用這些方法通過設定固定的時間間隔 1 來體現：

t <- trajectory() %>%
  deactivate(source = "dummy") %>%
  timeout(1) %>%
  activate(source = "dummy")

simmer() %>%
  add_generator("dummy", t, function() 1) %>%
  run(10) %>%
  get_mon_arrivals()
#>     name start_time end_time activity_time finished replication
#> 1 dummy0          1        2             1     TRUE           1
#> 2 dummy1          3        4             1     TRUE           1
#> 3 dummy2          5        6             1     TRUE           1
#> 4 dummy3          7        8             1     TRUE           1

`set_trajectory(), set_source()`

set_trajectory(., source, trajectory)和 set_source(., source, object) 方法提供了單獨地軌跡切換的方法。 source 可以是一個固定的字串ID也可以通過函式動態生成字串ID。

在下面的分佈中，t2 切換分佈到 t1，t2 只有首次到達時被執行。


t1 <- trajectory() %>%
  timeout(1)

t2 <- trajectory() %>%
  set_source("dummy", function() 1) %>%
  set_trajectory("dummy", t1) %>%
  timeout(2)

simmer() %>%
  add_generator("dummy", trajectory = t2, distribution = function() 2) %>%
  run(10) %>%
  get_mon_arrivals()
#>     name start_time end_time activity_time finished replication
#> 1 dummy0          2        4             2     TRUE           1
#> 2 dummy1          3        4             1     TRUE           1
#> 3 dummy2          4        5             1     TRUE           1
#> 4 dummy3          5        6             1     TRUE           1
#> 5 dummy4          6        7             1     TRUE           1
#> 6 dummy5          7        8             1     TRUE           1
#> 7 dummy6          8        9             1     TRUE           1

`set_prioritization()`

add_generator() 通過給到達流賦予優先順序的方式控制。set_prioritization(., values)和 get_prioritization(.) 方法可以在軌跡中的任意一個節點中改變/獲取優先順序。

  set_attribute("priority", 3) %>%
  # static values
  set_prioritization(values = c(3, 7, TRUE)) %>%
  # dynamically with a function
  set_prioritization(values = function() {
    prio <- get_prioritization(env)
    attr <- get_attribute(env, "priority")
    c(attr, prio[[2]]+1, FALSE)
  })

`branch()`

The branch(., option, continue, ...) 提供在軌跡中️以一定概率新增替代路徑的方法。下面的例子顯示一個到達在軌跡中被隨機分叉：

t1 <- trajectory("trajectory with a branch") %>%
  seize("server", 1) %>%
  branch(option = function() sample(1:2, 1), continue = c(T, F), 
         trajectory("branch1") %>%
           timeout(function() 1),
         trajectory("branch2") %>%
           timeout(function() rexp(1, 3)) %>%
           release("server", 1)
  ) %>%
  release("server", 1)

當到達流被分叉，第一個引數 option 是用來傳後續的具體路徑的概率值，因此它必須是可執行的，返回值需要是在1到n條路徑之間。第二個引數 continue 表示在選擇路徑後是否到達必須繼續執行活動。上述例子中，只有第一個路徑會走到最後的 release() 流程。

有時，你可能需要統計一條確定軌跡在一個確定的分支上進入多少次，或者到達流進入那條軌跡花費了多少時間。對於這種場景，處於計數需求，可以資源容量設定為無限，如下舉例：

t0 <- trajectory() %>%
  branch(function() sample(c(1, 2), 1), continue = c(T, T),
         trajectory() %>%
           seize("branch1", 1) %>%
           # do stuff here
           timeout(function() rexp(1, 1)) %>%
           release("branch1", 1),
         trajectory() %>%
           seize("branch2", 1) %>%
           # do stuff here
           timeout(function() rexp(1, 1/2)) %>%
           release("branch2", 1))

env <- simmer() %>%
  add_generator("dummy", t0, at(rep(0, 1000))) %>%
  # Resources with infinite capacity, just for accounting purposes
  add_resource("branch1", Inf) %>%
  add_resource("branch2", Inf) %>%
  run()

arrivals <- get_mon_arrivals(env, per_resource = T)

# Times that each branch was entered
table(arrivals$resource)
#> 
#> branch1 branch2 
#>     496     504

# The `activity_time` is the total time inside each branch for each arrival
# Let's see the distributions
ggplot(arrivals) + geom_histogram(aes(x=activity_time)) + facet_wrap(~resource)

`rollback()`

rollback(., amount, times, check) 回滾方法允許到達流軌跡回滾若干步，比如一個字串在超時函式中被列印出來，在第一次執行後，軌跡再回滾3次（因此總共列印 "Hello" 4次）。

t0 <- trajectory() %>%
  log_("Hello!") %>%
  timeout(1) %>%
  rollback(amount = 2, times = 3)

simmer() %>%
  add_generator("hello_sayer", t0, at(0)) %>% 
  run() %>% invisible
#> 0: hello_sayer0: Hello!
#> 1: hello_sayer0: Hello!
#> 2: hello_sayer0: Hello!
#> 3: hello_sayer0: Hello!

rollback() 方法也接受一個選項 check 來覆蓋預設的基於數值的行為。該方法可以傳入一個返回邏輯值的函式。每次一個到達接收到活動，check 都會判斷一下是否以指定步長呼叫 rollback()回滾：

t0 <- trajectory() %>%
  set_attribute("happiness", 0) %>%
  log_(function() {
    level <- get_attribute(env, "happiness")
    paste0(">> Happiness level is at: ", level, " -- ", 
           ifelse(level<25,"PETE: I'm feeling crappy...",
                  ifelse(level<50,"PETE: Feelin' a bit moody",
                         ifelse(level<75,"PETE: Just had a good espresso",
                                "PETE: Let's do this! (and stop this loop...)"))))
  }) %>%
  set_attribute("happiness", 25, mod="+") %>%
  rollback(amount = 2, check = function() get_attribute(env, "happiness") < 100)

env <- simmer() %>%
  add_generator("mood_swinger", t0, at(0))
env %>% run() %>% invisible()
#> 0: mood_swinger0: >> Happiness level is at: 0 -- PETE: I'm feeling crappy...
#> 0: mood_swinger0: >> Happiness level is at: 25 -- PETE: Feelin' a bit moody
#> 0: mood_swinger0: >> Happiness level is at: 50 -- PETE: Just had a good espresso
#> 0: mood_swinger0: >> Happiness level is at: 75 -- PETE: Let's do this! (and stop this loop...)

`leave()`

leave(., prob) 允許一個到達以一定概率離開整個軌跡：

patient_traj <- trajectory(name = "patient_trajectory") %>%
  seize("nurse", 1) %>%
  timeout(3) %>%
  release("nurse", 1) %>%
  log_("before leave") %>%
  leave(prob = 1) %>%
  log_("after leave") %>%
  # patients will never seize the doctor
  seize("doctor", 1) %>%
  timeout(3) %>%
  release("doctor", 1)

env <- simmer() %>%
  add_resource("nurse", capacity=1) %>%
  add_resource("doctor", capacity=1) %>%
  add_generator("patient", patient_traj, at(0)) %>%
  run()
#> 3: patient0: before leave

get_mon_resources(env)
#>   resource time server queue capacity queue_size system limit replication
#> 1    nurse    0      1     0        1        Inf      1   Inf           1
#> 2    nurse    3      0     0        1        Inf      0   Inf           1

當然, 概率也可以動態調整:

set.seed(1234)

patient_traj <- trajectory(name = "patient_trajectory") %>%
  seize("nurse", 1) %>%
  timeout(3) %>%
  release("nurse", 1) %>%
  log_("before leave") %>%
  leave(prob = function() runif(1) < 0.5) %>%
  log_("after leave") %>%
  # some patients will seize the doctor
  seize("doctor", 1) %>%
  timeout(3) %>%
  release("doctor", 1)

env <- simmer() %>%
  add_resource("nurse", capacity=1) %>%
  add_resource("doctor", capacity=1) %>%
  add_generator("patient", patient_traj, at(0, 1)) %>%
  run()
#> 3: patient0: before leave
#> 6: patient1: before leave
#> 6: patient1: after leave

get_mon_arrivals(env)
#>       name start_time end_time activity_time finished replication
#> 1 patient0          0        3             3    FALSE           1
#> 2 patient1          1        9             6     TRUE           1
get_mon_resources(env)
#>   resource time server queue capacity queue_size system limit replication
#> 1    nurse    0      1     0        1        Inf      1   Inf           1
#> 2    nurse    1      1     1        1        Inf      2   Inf           1
#> 3    nurse    3      1     0        1        Inf      1   Inf           1
#> 4    nurse    6      0     0        1        Inf      0   Inf           1
#> 5   doctor    6      1     0        1        Inf      1   Inf           1
#> 6   doctor    9      0     0        1        Inf      0   Inf           1

`clone(), synchronize()`

clone(., n, ...) 提供複製 n-1 次到達概率的方法來並行處理子軌跡。
synchronize(., wait, mon_all) 提供同步的方法來去除副本。預設，synchronize() 等待所有副本到達並且允許最後一個繼續執行：

t <- trajectory() %>%
  clone(n = 3,
        trajectory("original") %>%
          timeout(1),
        trajectory("clone 1") %>%
          timeout(2),
        trajectory("clone 2") %>%
          timeout(3)) %>%
  synchronize(wait = TRUE) %>%
  timeout(0.5)

env <- simmer(verbose = TRUE) %>%
  add_generator("arrival", t, at(0)) %>%
  run()
#>          0 |    source: arrival          |       new: arrival0         | 0
#>          0 |   arrival: arrival0         |  activity: Clone            | 3, 3 paths
#>          0 |   arrival: arrival0         |  activity: Timeout          | 1
#>          0 |   arrival: arrival0         |  activity: Timeout          | 2
#>          0 |   arrival: arrival0         |  activity: Timeout          | 3
#>          1 |   arrival: arrival0         |  activity: Synchronize      | 1
#>          2 |   arrival: arrival0         |  activity: Synchronize      | 1
#>          3 |   arrival: arrival0         |  activity: Synchronize      | 1
#>          3 |   arrival: arrival0         |  activity: Timeout          | 0.5

get_mon_arrivals(env)
#>       name start_time end_time activity_time finished replication
#> 1 arrival0          0      3.5           3.5     TRUE           1

注意，引數 n 也可以是一個函式，如果有子軌跡需要clone，那麼重複的子軌跡不需要反覆宣告。如果子軌跡數量小於 clone 數量，部分clone將直接繼續下一個行動：

t <- trajectory() %>%
  clone(n = 3,
        trajectory("original") %>%
          timeout(1),
        trajectory("clone 1") %>%
          timeout(2)) %>%
  synchronize(wait = TRUE) %>%
  timeout(0.5)

env <- simmer(verbose = TRUE) %>%
  add_generator("arrival", t, at(0)) %>%
  run()
#>          0 |    source: arrival          |       new: arrival0         | 0
#>          0 |   arrival: arrival0         |  activity: Clone            | 3, 2 paths
#>          0 |   arrival: arrival0         |  activity: Timeout          | 1
#>          0 |   arrival: arrival0         |  activity: Timeout          | 2
#>          0 |   arrival: arrival0         |  activity: Synchronize      | 1
#>          1 |   arrival: arrival0         |  activity: Synchronize      | 1
#>          2 |   arrival: arrival0         |  activity: Synchronize      | 1
#>          2 |   arrival: arrival0         |  activity: Timeout          | 0.5

get_mon_arrivals(env)
#>       name start_time end_time activity_time finished replication
#> 1 arrival0          0      2.5           2.5     TRUE           1

如果預期為弱依賴，希望最快完成副本任務，那麼 synchronize() 可以設定 wait = FALSE:

t <- trajectory() %>%
  clone(n = 3,
        trajectory("original") %>%
          timeout(1),
        trajectory("clone 1") %>%
          timeout(2),
        trajectory("clone 2") %>%
          timeout(3)) %>%
  synchronize(wait = FALSE) %>%
  timeout(0.5)

env <- simmer(verbose = TRUE) %>%
  add_generator("arrival", t, at(0)) %>%
  run()
#>          0 |    source: arrival          |       new: arrival0         | 0
#>          0 |   arrival: arrival0         |  activity: Clone            | 3, 3 paths
#>          0 |   arrival: arrival0         |  activity: Timeout          | 1
#>          0 |   arrival: arrival0         |  activity: Timeout          | 2
#>          0 |   arrival: arrival0         |  activity: Timeout          | 3
#>          1 |   arrival: arrival0         |  activity: Synchronize      | 0
#>          1 |   arrival: arrival0         |  activity: Timeout          | 0.5
#>          2 |   arrival: arrival0         |  activity: Synchronize      | 0
#>          3 |   arrival: arrival0         |  activity: Synchronize      | 0

get_mon_arrivals(env)
#>       name start_time end_time activity_time finished replication
#> 1 arrival0          0      1.5           1.5     TRUE           1

synchronize() 預設不記錄被移除的 clone資訊 (mon_all=FALSE)，但是如果需要可以通過修改 mon_all=TRUE 來實現:

t <- trajectory() %>%
  clone(n = 3,
        trajectory("original") %>%
          timeout(1),
        trajectory("clone 1") %>%
          timeout(2),
        trajectory("clone 2") %>%
          timeout(3)) %>%
  synchronize(wait = FALSE, mon_all = TRUE) %>%
  timeout(0.5)

env <- simmer(verbose = TRUE) %>%
  add_generator("arrival", t, at(0)) %>%
  run()
#>          0 |    source: arrival          |       new: arrival0         | 0
#>          0 |   arrival: arrival0         |  activity: Clone            | 3, 3 paths
#>          0 |   arrival: arrival0         |  activity: Timeout          | 1
#>          0 |   arrival: arrival0         |  activity: Timeout          | 2
#>          0 |   arrival: arrival0         |  activity: Timeout          | 3
#>          1 |   arrival: arrival0         |  activity: Synchronize      | 0
#>          1 |   arrival: arrival0         |  activity: Timeout          | 0.5
#>          2 |   arrival: arrival0         |  activity: Synchronize      | 0
#>          3 |   arrival: arrival0         |  activity: Synchronize      | 0

get_mon_arrivals(env)
#>       name start_time end_time activity_time finished replication
#> 1 arrival0          0      1.5           1.5     TRUE           1
#> 2 arrival0          0      2.0           2.0     TRUE           1
#> 3 arrival0          0      3.0           3.0     TRUE           1

`batch(), separate()`

batch(., n, timeout, permanent, name, rule) 提供以一定概率收集一定數量的到達流後批量處理的方法。然後，通過 separate(.) 方法來分離之前建立的臨時批次。這允許我們實現一個過山車過程，舉例：

有一個10人座的過山車，佇列是20人排隊參與，每次玩過山車持續5分鐘，我們可以將問題按如下方式建模：

set.seed(1234)

t <- trajectory() %>%
  batch(10, timeout = 5, permanent = FALSE) %>%
  seize("rollercoaster", 1) %>%
  timeout(5) %>%
  release("rollercoaster", 1) %>%
  separate()

env <- simmer() %>%
  # capacity and queue_size are defined in batches of 10
  add_resource("rollercoaster", capacity = 1, queue_size = 2) %>%
  add_generator("person", t, function() rexp(1, 2)) %>%
  run(15)

get_mon_arrivals(env, per_resource = TRUE)
#>        name start_time  end_time activity_time      resource replication
#> 1   person0   3.800074  8.800074             5 rollercoaster           1
#> 2   person1   3.800074  8.800074             5 rollercoaster           1
#> 3   person2   3.800074  8.800074             5 rollercoaster           1
#> 4   person3   3.800074  8.800074             5 rollercoaster           1
#> 5   person4   3.800074  8.800074             5 rollercoaster           1
#> 6   person5   3.800074  8.800074             5 rollercoaster           1
#> 7   person6   3.800074  8.800074             5 rollercoaster           1
#> 8   person7   3.800074  8.800074             5 rollercoaster           1
#> 9   person8   3.800074  8.800074             5 rollercoaster           1
#> 10  person9   3.800074  8.800074             5 rollercoaster           1
#> 11 person10   8.800074 13.800074             5 rollercoaster           1
#> 12 person11   8.800074 13.800074             5 rollercoaster           1
#> 13 person12   8.800074 13.800074             5 rollercoaster           1
#> 14 person13   8.800074 13.800074             5 rollercoaster           1
#> 15 person14   8.800074 13.800074             5 rollercoaster           1
#> 16 person15   8.800074 13.800074             5 rollercoaster           1

這裡建立了 3 個批次，前10個人都是在3.8分鐘同時上車的。然後在第一波遊玩結束時, 只有6個人在等待，但是 batch() 設定的計時器 timeout=5 已經到時了，另外一波遊客就可以發動了。因為這個 batch設定了 (permanent=FALSE)，所以可以用 separate() 方法將佇列切開。

當然具體的rule引數也可以用更精細粒度的選擇哪些遊客需要被組成一個批次。對於每個特定的到達，預設都是一 rule = TRUE 返回。上面的例子，也可以通過 rule = FALSE，避免和其他乘客同時玩一個過山車。

t_batch <- trajectory() %>%
  batch(10, timeout = 5, permanent = FALSE, rule = function() FALSE) %>%
  seize("rollercoaster", 1) %>%
  timeout(5) %>%
  release("rollercoaster", 1) %>%
  separate()

t_nobatch <- trajectory() %>%
  seize("rollercoaster", 1) %>%
  timeout(5) %>%
  release("rollercoaster", 1)

set.seed(1234)

env_batch <- simmer() %>%
  # capacity and queue_size are defined in batches of 10
  add_resource("rollercoaster", capacity = 1, queue_size = 2) %>%
  add_generator("person", t_batch, function() rexp(1, 2)) %>%
  run(15)

set.seed(1234)

env_nobatch <- simmer() %>%
  # capacity and queue_size are defined in batches of 10
  add_resource("rollercoaster", capacity = 1, queue_size = 2) %>%
  add_generator("person", t_nobatch, function() rexp(1, 2)) %>%
  run(15)

get_mon_arrivals(env_batch, per_resource = TRUE)
#>      name start_time  end_time activity_time      resource replication
#> 1 person0   1.250879  6.250879             5 rollercoaster           1
#> 2 person1   1.374259 11.250879             5 rollercoaster           1
get_mon_arrivals(env_nobatch, per_resource = TRUE)
#>      name start_time  end_time activity_time      resource replication
#> 1 person0   1.250879  6.250879             5 rollercoaster           1
#> 2 person1   1.374259 11.250879             5 rollercoaster           1

預設，批次的 name 引數為空，它表示每個乘客是獨立的，但是，有趣的是怎麼給不同軌跡賦予相同批次。比如，我們可以嘗試：

t0 <- trajectory() %>%
  batch(2) %>%
  timeout(2) %>%
  separate()

t1 <- trajectory() %>%
  timeout(1) %>%
  join(t0)

env <- simmer(verbose = TRUE) %>%
  add_generator("t0_", t0, at(0)) %>%
  add_generator("t1_", t1, at(0)) %>%
  run()
#>          0 |    source: t0_              |       new: t0_0             | 0
#>          0 |   arrival: t0_0             |  activity: Batch            | 2, 0, 0, 
#>          0 |    source: t1_              |       new: t1_0             | 0
#>          0 |   arrival: t1_0             |  activity: Timeout          | 1
#>          1 |   arrival: t1_0             |  activity: Batch            | 2, 0, 0,

get_mon_arrivals(env)
#> [1] name          start_time    end_time      activity_time finished     
#> <0 rows> (or 0-length row.names)

我們沒有獲得預期的兩個不同批次結果。t1 緊跟著 t0 到達，則意味著實際情況是下面這樣：

t0 <- trajectory() %>%
  batch(2) %>%
  timeout(2) %>%
  separate()

t1 <- trajectory() %>%
  timeout(1) %>%
  batch(2) %>%
  timeout(2) %>%
  separate()

因此到達流緊隨著一個不同軌跡將終止在一個不同批次上。除非，有一個方法共享 batch()的行動，現在可以通過 name 引數實現。

t0 <- trajectory() %>%
  batch(2, name = "mybatch") %>%
  timeout(2) %>%
  separate()

t1 <- trajectory() %>%
  timeout(1) %>%
  batch(2, name = "mybatch") %>%
  timeout(2) %>%
  separate()

env <- simmer(verbose = TRUE) %>%
  add_generator("t0_", t0, at(0)) %>%
  add_generator("t1_", t1, at(0)) %>%
  run()
#>          0 |    source: t0_              |       new: t0_0             | 0
#>          0 |   arrival: t0_0             |  activity: Batch            | 2, 0, 0, mybatch
#>          0 |    source: t1_              |       new: t1_0             | 0
#>          0 |   arrival: t1_0             |  activity: Timeout          | 1
#>          1 |   arrival: t1_0             |  activity: Batch            | 2, 0, 0, mybatch
#>          1 |   arrival: batch_mybatch    |  activity: Timeout          | 2
#>          3 |   arrival: batch_mybatch    |  activity: Separate         |

get_mon_arrivals(env)
#>   name start_time end_time activity_time finished replication
#> 1 t0_0          0        3             2     TRUE           1
#> 2 t1_0          0        3             3     TRUE           1
Or, equivalently,

t0 <- trajectory() %>%
  batch(2, name = "mybatch") %>%
  timeout(2) %>%
  separate()

t1 <- trajectory() %>%
  timeout(1) %>%
  join(t0)

env <- simmer(verbose = TRUE) %>%
  add_generator("t0_", t0, at(0)) %>%
  add_generator("t1_", t1, at(0)) %>%
  run()
#>          0 |    source: t0_              |       new: t0_0             | 0
#>          0 |   arrival: t0_0             |  activity: Batch            | 2, 0, 0, mybatch
#>          0 |    source: t1_              |       new: t1_0             | 0
#>          0 |   arrival: t1_0             |  activity: Timeout          | 1
#>          1 |   arrival: t1_0             |  activity: Batch            | 2, 0, 0, mybatch
#>          1 |   arrival: batch_mybatch    |  activity: Timeout          | 2
#>          3 |   arrival: batch_mybatch    |  activity: Separate         |

get_mon_arrivals(env)
#>   name start_time end_time activity_time finished replication
#> 1 t0_0          0        3             2     TRUE           1
#> 2 t1_0          0        3             3     TRUE           1

`send(), trap(), untrap(), wait()`

這組行動允許非同步程式設計。通過 send(., signals, delay) 廣播一個或者一組訊號給到每個訂閱資訊的到達流。訊號可以立即被觸發：

t <- trajectory() %>%
  send(signals = c("signal1", "signal2"))

simmer(verbose = TRUE) %>%
  add_generator("signaler", t, at(0)) %>%
  run() %>% invisible
#>          0 |    source: signaler         |       new: signaler0        | 0
#>          0 |   arrival: signaler0        |  activity: Send             | [signal1, signal2], 0
#>          0 |      task: Broadcast        |          :                  |

或者安排在一些延遲之後：

t <- trajectory() %>%
  send(signals = c("signal1", "signal2"), delay = 3)

simmer(verbose = TRUE) %>%
  add_generator("signaler", t, at(0)) %>%
  run() %>% invisible
#>          0 |    source: signaler         |       new: signaler0        | 0
#>          0 |   arrival: signaler0        |  activity: Send             | [signal1, signal2], 3
#>          3 |      task: Broadcast        |          :                  |

注意,這兩個引數,signals和delay,可以是函式,因此他們可以從到達流中獲取的屬性值。

如果無人監聽，廣播其實沒意義。到達流訂閱廣播然後可以用 trap(., signals, handler, interruptible) 來賦予一個處理器。在下面的例子中，一個到達流訂閱一個訊號並且阻塞知道收到 wait(.) 方法。

t_blocked <- trajectory() %>%
  trap("you shall pass") %>%
  log_("waiting...") %>%
  wait() %>%
  log_("continuing!")

t_signaler <- trajectory() %>%
  log_("you shall pass") %>%
  send("you shall pass")

simmer() %>%
  add_generator("blocked", t_blocked, at(0)) %>%
  add_generator("signaler", t_signaler, at(5)) %>%
  run() %>% invisible
#> 0: blocked0: waiting...
#> 5: signaler0: you shall pass
#> 5: blocked0: continuing!

注意訊號可以被忽略，當到達流是在資源佇列中等待。相同的操作也可以在批處理中執行：所有在進入批次之前的被訂閱資訊都將被忽略。因此，下面的批次將被無限阻塞：

t_blocked <- trajectory() %>%
  trap("you shall pass") %>%
  log_("waiting inside a batch...") %>%
  batch(1) %>%
  wait() %>%
  log_("continuing!")

t_signaler <- trajectory() %>%
  log_("you shall pass") %>%
  send("you shall pass")

simmer() %>%
  add_generator("blocked", t_blocked, at(0)) %>%
  add_generator("signaler", t_signaler, at(5)) %>%
  run() %>% invisible
#> 0: blocked0: waiting inside a batch...
#> 5: signaler0: you shall pass
#> inf: batch0: continuing!

在接收訊號,停止當前活動並執行處理程式提供。然後,執行後返回到活動中斷的點:

t_worker <- trajectory() %>%
  trap("you are free to go", 
       handler = trajectory() %>%
         log_("ok, I'm packing...") %>%
         timeout(1)
  ) %>%
  log_("performing a looong task...") %>%
  timeout(100) %>%
  log_("and I'm leaving!")

t_signaler <- trajectory() %>%
  log_("you are free to go") %>%
  send("you are free to go")

simmer() %>%
  add_generator("worker", t_worker, at(0)) %>%
  add_generator("signaler", t_signaler, at(5)) %>%
  run() %>% invisible
#> 0: worker0: performing a looong task...
#> 5: signaler0: you are free to go
#> 5: worker0: ok, I'm packing...
#> 6: worker0: and I'm leaving!

最後，untrap(., signals) 來根據 signals 執行退訂：

t_worker <- trajectory() %>%
  trap("you are free to go", 
       handler = trajectory() %>%
         log_("ok, I'm packing...") %>%
         timeout(1)
  ) %>%
  log_("performing a looong task...") %>%
  untrap("you are free to go") %>%
  timeout(100) %>%
  log_("and I'm leaving!")

t_signaler <- trajectory() %>%
  log_("you are free to go") %>%
  send("you are free to go")

simmer() %>%
  add_generator("worker", t_worker, at(0)) %>%
  add_generator("signaler", t_signaler, at(5)) %>%
  run() %>% invisible
#> 0: worker0: performing a looong task...
#> 5: signaler0: you are free to go
#> 100: worker0: and I'm leaving!

Signal 處理器預設是可以被打斷，這意味著如果有大量頻繁的請求訊號，處理器會反覆重啟：

t_worker <- trajectory() %>%
  trap("you are free to go", 
       handler = trajectory() %>%
         log_("ok, I'm packing...") %>%
         timeout(1)
  ) %>%
  log_("performing a looong task...") %>%
  timeout(100) %>%
  log_("and I'm leaving!")

t_signaler <- trajectory() %>%
  log_("you are free to go") %>%
  send("you are free to go")

simmer() %>%
  add_generator("worker", t_worker, at(0)) %>%
  add_generator("signaler", t_signaler, from(5, function() 0.5)) %>%
  run(10) %>% invisible
#> 0: worker0: performing a looong task...
#> 5: signaler0: you are free to go
#> 5: worker0: ok, I'm packing...
#> 5.5: signaler1: you are free to go
#> 5.5: worker0: ok, I'm packing...
#> 6: signaler2: you are free to go
#> 6: worker0: ok, I'm packing...
#> 6.5: signaler3: you are free to go
#> 6.5: worker0: ok, I'm packing...
#> 7: signaler4: you are free to go
#> 7: worker0: ok, I'm packing...
#> 7.5: signaler5: you are free to go
#> 7.5: worker0: ok, I'm packing...
#> 8: signaler6: you are free to go
#> 8: worker0: ok, I'm packing...
#> 8.5: signaler7: you are free to go
#> 8.5: worker0: ok, I'm packing...
#> 9: signaler8: you are free to go
#> 9: worker0: ok, I'm packing...
#> 9.5: signaler9: you are free to go
#> 9.5: worker0: ok, I'm packing...

如果需要實現一個不能打斷的處理器，可以通過設定合適的 flag 實現：

t_worker <- trajectory() %>%
  trap("you are free to go", 
       handler = trajectory() %>%
         log_("ok, I'm packing...") %>%
         timeout(1),
       interruptible = FALSE            # make it uninterruptible
  ) %>%
  log_("performing a looong task...") %>%
  timeout(100) %>%
  log_("and I'm leaving!")

t_signaler <- trajectory() %>%
  log_("you are free to go") %>%
  send("you are free to go")

simmer() %>%
  add_generator("worker", t_worker, at(0)) %>%
  add_generator("signaler", t_signaler, from(5, function() 0.5)) %>%
  run(10) %>% invisible
#> 0: worker0: performing a looong task...
#> 5: signaler0: you are free to go
#> 5: worker0: ok, I'm packing...
#> 5.5: signaler1: you are free to go
#> 6: worker0: and I'm leaving!
#> 6: signaler2: you are free to go
#> 6.5: signaler3: you are free to go
#> 7: signaler4: you are free to go
#> 7.5: signaler5: you are free to go
#> 8: signaler6: you are free to go
#> 8.5: signaler7: you are free to go
#> 9: signaler8: you are free to go
#> 9.5: signaler9: you are free to go

`renege_in(), renege_if(), renege_abort()`

renege_in(., t, out) 方法提供設定超時時間來出發到達流放棄軌跡的退出機制。中途退出後，到達流可以選擇從一個子軌跡中出去。renege_abort(.) 方法提供了一個反悔機制。這些方法允許我們做一些事情，比如，建立有限病人的模型。下面的例子中，使用者到達銀行，只有一個職員處於服務態。客服在等待5分鐘後如果還不能服務可以選擇離開。

t <- trajectory(name = "bank") %>%
  log_("Here I am") %>%
  # renege in 5 minutes
  renege_in(5, 
            out = trajectory() %>%
              log_("Lost my patience. Reneging...")
  ) %>%
  seize("clerk", 1) %>%
  # stay if I'm being attended within 5 minutes
  renege_abort() %>%
  log_("I'm being attended") %>%
  timeout(10) %>%
  release("clerk", 1) %>%
  log_("Finished")

simmer() %>%
  add_resource("clerk", 1) %>%
  add_generator("customer", t, at(0, 1)) %>%
  run() %>% invisible
#> 0: customer0: Here I am
#> 0: customer0: I'm being attended
#> 1: customer1: Here I am
#> 6: customer1: Lost my patience. Reneging...
#> 10: customer0: Finished

同樣也可以通過 renege_if(., signal, out) 實現，假設在 t=5時刻， customer0 傳送一個訊息給 customer1:

t <- trajectory(name = "bank") %>%
  log_("Here I am") %>%
  # renege when "renege now" is received
  renege_if("renege now", 
            out = trajectory() %>%
              log_("Ok. Reneging...")
  ) %>%
  seize("clerk", 1) %>%
  # stay if I'm being attended within 5 minutes
  renege_abort() %>%
  log_("I'm being attended") %>%
  timeout(5) %>%
  log_("I say: renege now") %>%
  send("renege now") %>%
  timeout(5) %>%
  release("clerk", 1) %>%
  log_("Finished")

simmer() %>%
  add_resource("clerk", 1) %>%
  add_generator("customer", t, at(0, 1)) %>%
  run() %>% invisible
#> 0: customer0: Here I am
#> 0: customer0: I'm being attended
#> 1: customer1: Here I am
#> 5: customer0: I say: renege now
#> 5: customer1: Ok. Reneging...
#> 10: customer0: Finished

注意，和 trap() 不同的是, reneg* 是直接被觸發的，即使到達流還在佇列或者臨時批次中。

軌跡工具箱: joining 和 subsetting

`join()`

join(...) 將任意多個軌跡聚合，比如：

t1 <- trajectory() %>% seize("dummy", 1)
t2 <- trajectory() %>% timeout(1)
t3 <- trajectory() %>% release("dummy", 1)

t0 <- join(t1, t2, t3)
t0
#> trajectory: anonymous, 3 activities
#> { Activity: Seize        | resource: dummy, amount: 1 }
#> { Activity: Timeout      | delay: 1 }
#> { Activity: Release      | resource: dummy, amount: 1 }

或者，它可能巢狀使用，類似另一個行為：

t0 <- trajectory() %>%
  join(t1) %>%
  timeout(1) %>%
  join(t3)
t0
#> trajectory: anonymous, 3 activities
#> { Activity: Seize        | resource: dummy, amount: 1 }
#> { Activity: Timeout      | delay: 1 }
#> { Activity: Release      | resource: dummy, amount: 1 }

參考資料

原文作者： Iñaki Ucar, Bart Smeets 譯者： Harry Zhu 英文原文地址:
https://r-simmer.org/articles...
作為分享主義者(sharism)，本人所有網際網路釋出的圖文均遵從CC版權，轉載請保留作者資訊並註明作者 Harry Zhu 的 FinanceR專欄:https://segmentfault.com/blog...，如果涉及原始碼請註明GitHub地址：https://github.com/harryprince。微訊號: harryzhustudio
商業使用請聯絡作者。

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[譯] 強化學習入門篇：Simmer 模擬平臺高階使用技巧

如何與環境互動

行動集合

log_()

set_attribute(), set_global()

timeout(), timeout_from_attribute()

seize(), release()

set_capacity(), set_queue_size()

select()

activate(), deactivate()

set_trajectory(), set_source()

set_prioritization()

branch()

rollback()

leave()

clone(), synchronize()

batch(), separate()

send(), trap(), untrap(), wait()

renege_in(), renege_if(), renege_abort()