configurable cap of bounded #71

border-async-trainer/src/actor_manager/base.rs

@@ -40,6 +40,9 @@ where
     /// This parameter is used as `n_buffer` in [`ReplayBufferProxyConfig`].
     n_buffer: usize,
 
+    /// capacity of channel between each actor and actor-manager
+    channel_capacity: usize,
+
     /// Flag to stop training
     stop: Arc<Mutex<bool>>,
 
@@ -90,6 +93,7 @@ where
             env_config: env_config.clone(),
             step_proc_config: step_proc_config.clone(),
             n_buffer: config.n_buffer,
+            channel_capacity: config.channel_capacity,
             stop,
             threads: vec![],
             batch_message_receiver: None,
@@ -130,7 +134,7 @@ where
 
         // Create channel for [BatchMessage]
         // let (s, r) = unbounded();
-        let (s, r) = bounded(1000);
+        let (s, r) = bounded(self.channel_capacity);
         self.batch_message_receiver = Some(r.clone());
 
         // Runs sampling processes

border-async-trainer/src/actor_manager/config.rs

@@ -7,12 +7,16 @@ pub struct ActorManagerConfig {
     ///
     /// The default value is 100.
     pub n_buffer: usize,
+
+    /// capacity of channel between each actor and actor-manager
+    pub channel_capacity: usize,
 }
 
 impl Default for ActorManagerConfig {
     fn default()  -> Self {
         Self {
             n_buffer: 100,
+            channel_capacity: 1000,
         }
     }
 }

border-async-trainer/src/async_trainer/config.rs

@@ -29,6 +29,9 @@ pub struct AsyncTrainerConfig {
 
     /// The number of episodes for evaluation
     pub eval_episodes: usize,
+
+    /// capacity of channel between each actor-manager and async-trainer
+    pub channel_capacity: usize,
 }
 
 impl AsyncTrainerConfig {

border-async-trainer/src/util.rs

@@ -57,7 +57,7 @@ pub fn train_async<A, E, R, S, P>(
 
     // Creates channels
     // let (item_s, item_r) = unbounded(); // items pushed to replay buffer
-    let (item_s, item_r) = bounded(50); // items pushed to replay buffer
+    let (item_s, item_r) = bounded(async_trainer_config.channel_capacity); // items pushed to replay buffer
     let (model_s, model_r) = unbounded(); // model_info
 
     // guard for initialization of envs in multiple threads