memory_size=5000,
batch_size=320,
# output_graph=True
)
step = 0
# total train episode
for episode in range(4):
# for each episode train all the user onece
episode_start_time = time.time()
for currentUserId in trainUserIdRange + 1: #current_Env.numUser
currentSeqIndex = 0
observation = current_Env.generateInputVector( currentUserId, currentSeqIndex )
while True:
# RL choose action based on observation which its a index number
flag, actionIndex = RL.choose_action(observation)
action = current_Env.actionTransform(actionIndex)
# RL take action and get next observation and reward
ifTerminal, currentSeqIndex, observation_, reward = current_Env.update(currentUserId, currentSeqIndex, action)
# Experience replay
RL.store_transition(observation, actionIndex, reward, observation_)
if (step > 200) and (step % 5 == 0):
RL.learn()
# swap observation
observation = observation_
step += 1
# break while loop when end of this episode
if ifTerminal:
print("User: "******" Done")
break
