From b8bd169722e591099a26765bc771bfa0be0cddf8 Mon Sep 17 00:00:00 2001
From: ainilaha <281761274@qq.com>
Date: Fri, 13 Nov 2020 13:51:25 -0600
Subject: [PATCH] fix a minor issue replay.py

---
 deep_rl/component/replay.py | 4 ++--
 examples.py                 | 8 ++++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/deep_rl/component/replay.py b/deep_rl/component/replay.py
index d688a63c..54168f54 100644
--- a/deep_rl/component/replay.py
+++ b/deep_rl/component/replay.py
@@ -202,12 +202,12 @@ class ReplayWrapper(mp.Process):
     EXIT = 2
     UPDATE_PRIORITIES = 3
 
-    def __init__(self, replay_cls, replay_kwargs, async=True):
+    def __init__(self, replay_cls, replay_kwargs, async_flag=True):
         mp.Process.__init__(self)
         self.replay_kwargs = replay_kwargs
         self.replay_cls = replay_cls
         self.cache_len = 2
-        if async:
+        if async_flag:
             self.pipe, self.worker_pipe = mp.Pipe()
             self.start()
         else:
diff --git a/examples.py b/examples.py
index c6e49a30..bc3f344e 100644
--- a/examples.py
+++ b/examples.py
@@ -113,7 +113,7 @@ def quantile_regression_dqn_feature(**kwargs):
     replay_kwargs = dict(
         memory_size=int(1e4),
         batch_size=config.batch_size)
-    config.replay_fn = lambda: ReplayWrapper(UniformReplay, replay_kwargs, async=True)
+    config.replay_fn = lambda: ReplayWrapper(UniformReplay, replay_kwargs)
 
     config.random_action_prob = LinearSchedule(1.0, 0.1, 1e4)
     config.discount = 0.99
@@ -146,7 +146,7 @@ def quantile_regression_dqn_pixel(**kwargs):
         batch_size=config.batch_size,
         history_length=4,
     )
-    config.replay_fn = lambda: ReplayWrapper(UniformReplay, replay_kwargs, async=True)
+    config.replay_fn = lambda: ReplayWrapper(UniformReplay, replay_kwargs)
 
     config.state_normalizer = ImageNormalizer()
     config.reward_normalizer = SignNormalizer()
@@ -177,7 +177,7 @@ def categorical_dqn_feature(**kwargs):
     replay_kwargs = dict(
         memory_size=int(1e4),
         batch_size=config.batch_size)
-    config.replay_fn = lambda: ReplayWrapper(UniformReplay, replay_kwargs, async=True)
+    config.replay_fn = lambda: ReplayWrapper(UniformReplay, replay_kwargs)
 
     config.discount = 0.99
     config.target_network_update_freq = 200
@@ -211,7 +211,7 @@ def categorical_dqn_pixel(**kwargs):
         batch_size=config.batch_size,
         history_length=4,
     )
-    config.replay_fn = lambda: ReplayWrapper(UniformReplay, replay_kwargs, async=True)
+    config.replay_fn = lambda: ReplayWrapper(UniformReplay, replay_kwargs)
 
     config.discount = 0.99
     config.state_normalizer = ImageNormalizer()