/
run_this.py
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/
run_this.py
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from pocketAcesNet import PocketAces
from RL_brain import DeepQNetwork
from RL_brain import *
import random
import cv2
import time
# Built following the tutorial found at
# https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5_Deep_Q_Network
def run_pocket_aces():
step = 0
time.sleep(2) #allows user to click off into DD Poker 3
totalReward = 0
for episode in range(50000):
# saving, MAKE SURE SAVE DIRECTORY IS DELETED
if episode == 1000:
RL.save(observation, action, reward, observation_, "save/save0")
if episode == 3000:
RL.save(observation, action, reward, observation_, "save/save1")
if episode == 6000:
RL.save(observation, action, reward, observation_, "save/save2")
if episode == 10000:
RL.save(observation, action, reward, observation_, "save/save3")
if episode == 12000:
RL.save(observation, action, reward, observation_, "save/save4")
if episode == 15000:
RL.save(observation, action, reward, observation_, "save/save5")
if episode == 20000:
RL.save(observation, action, reward, observation_, "save/save6")
if episode == 25000:
RL.save(observation, action, reward, observation_, "save/save7")
if episode == 30000:
RL.save(observation, action, reward, observation_, "save/save8")
if episode == 35000:
RL.save(observation, action, reward, observation_, "save/save9")
if episode == 40000:
RL.save(observation, action, reward, observation_, "save/save10")
if episode == 45000:
RL.save(observation, action, reward, observation_, "save/save11")
if episode == 49000:
RL.save(observation, action, reward, observation_, "save/save12")
if episode == 50000:
RL.save(observation, action, reward, observation_, "save/save14")
# initial observation
observation = env.reset()
reward = 0
while True:
# gameStart(); commented for testing
# RL choose action based on observation
action = RL.choose_action(observation)
# RL take action and get next observation and reward
observation_, reward, done = env.step(action)
plotData = reward
totalReward = totalReward + plotData
f = open('csv/session3/_no_greed_Session3_loaded_TotalReward-positiveRewards.csv', 'a')
f.write(str(totalReward))
f.write('\n')
f.close()
f = open('csv/session3/_no_greed_Session3_loaded_AllInformation-positiveRewards.csv', 'a')
for value in observation_:
f.write(str(value) + ",")
f.write('\n')
f.close()
RL.store_transition(observation, action, reward, observation_)
if (step > 200) and (step % 10 == 0):
RL.learn()
# swap observation
observation = observation_
# break while loop when end of this episode
if done:
break
step = step + 1
if cv2.waitKey(0) & 0xFF == ord('q'):
break
# end of game
print('game over')
if __name__ == "__main__":
env = PocketAces()
RL = DeepQNetwork(env.n_actions, env.n_features,
learning_rate=0.01,
reward_decay=0.9,
e_greedy=1,
replace_target_iter=500,
memory_size=45000,
output_graph=True
)
run_pocket_aces()
RL.plot_cost()