fix #715

examples/A3C-Gym/README.md

@@ -23,7 +23,7 @@ because the bottleneck in this implementation is not computation but simulation.
 
 Some practicical notes:
 
-1. Prefer Python 3.
+1. Prefer Python 3; Windows not supported.
 2. Occasionally, processes may not get terminated completely. It is suggested to use `systemd-run` to run any
 multiprocess Python program to get a cgroup dedicated for the task.
 3. Training with a significant slower speed (e.g. on CPU) will result in very bad score, probably because of the slightly off-policy implementation.

examples/A3C-Gym/train-atari.py

@@ -231,8 +231,9 @@ def train():
 
     # setup simulator processes
     name_base = str(uuid.uuid1())[:6]
-    namec2s = 'ipc://@sim-c2s-{}'.format(name_base)
-    names2c = 'ipc://@sim-s2c-{}'.format(name_base)
+    prefix = '@' if sys.platform.startswith('linux') else ''
+    namec2s = 'ipc://{}sim-c2s-{}'.format(prefix, name_base)
+    names2c = 'ipc://{}sim-s2c-{}'.format(prefix, name_base)
     procs = [MySimulatorWorker(k, namec2s, names2c) for k in range(SIMULATOR_PROC)]
     ensure_proc_terminate(procs)
     start_proc_mask_signal(procs)