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Clean way to stop training #84

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Clean way to stop training #84

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6 changes: 4 additions & 2 deletions 2-cartpole/1-dqn/cartpole_dqn.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@
import sys
import gym
import pylab
import random
Expand Down Expand Up @@ -121,13 +120,16 @@ def train_model(self):
agent = DQNAgent(state_size, action_size)

scores, episodes = [], []
complete = False

for e in range(EPISODES):
done = False
score = 0
state = env.reset()
state = np.reshape(state, [1, state_size])

if complete: break

while not done:
if agent.render:
env.render()
Expand Down Expand Up @@ -162,7 +164,7 @@ def train_model(self):
# if the mean of scores of last 10 episode is bigger than 490
# stop training
if np.mean(scores[-min(10, len(scores)):]) > 490:
sys.exit()
complete = True
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why don't we just break here? and not use complete flag?


# save the model
if e % 50 == 0:
Expand Down