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-rw-r--r--candle-examples/examples/reinforcement-learning/atari_wrappers.py2
1 files changed, 1 insertions, 1 deletions
diff --git a/candle-examples/examples/reinforcement-learning/atari_wrappers.py b/candle-examples/examples/reinforcement-learning/atari_wrappers.py
index b5c4665d..b76fb85d 100644
--- a/candle-examples/examples/reinforcement-learning/atari_wrappers.py
+++ b/candle-examples/examples/reinforcement-learning/atari_wrappers.py
@@ -78,7 +78,7 @@ class EpisodicLifeEnv(gym.Wrapper):
# then update lives to handle bonus lives
lives = self.env.unwrapped.ale.lives()
if lives < self.lives and lives > 0:
- # for Qbert somtimes we stay in lives == 0 condtion for a few frames
+ # for Qbert sometimes we stay in lives == 0 condition for a few frames
# so its important to keep lives > 0, so that we only reset once
# the environment advertises done.
done = True