def testAlgo(init=0):
arrow = ["^", "v", "<", ">"]
i = 0
if init == 0:
state = initGrid()
elif init == 1:
state = initGridPlayer()
else:
state = initGridRand()
drawgridworld.draw_state(state, 0)
time.sleep(0.5)
#print("Initial State:")
#print(dispGrid(state))
status = 1
#while game still in progress
while (status == 1):
qval = model.predict(state.reshape(1, 64), batch_size=1)
action = (np.argmax(qval)) #take action with highest Q-value
print('Move #: %s; Taking action: %s' % (i, arrow[action]))
state = makeMove(state, action)
#print(dispGrid(state))
reward = getReward(state)
if reward != -1:
status = 0
print("Reward: %s" % (reward, ))
i += 1 #If we're taking more than 10 actions, just stop, we probably can't win this game
drawgridworld.draw_state(state, i - 10)
time.sleep(0.5)
if (i > 10):
print("Game lost; too many moves.")
reward = -10
break
return reward
def testAlgo(init=0):
i = 0
if init == 0:
state = initGrid()
elif init == 1:
state = initGridPlayer()
elif init == 2:
state = initGridRand()
str_to_print = 'Initial State:'
str_to_print += '\n%s' % dispGrid(state)
# while game still in progress
for i in range(10):
qval = model.predict(state.reshape(1, 64), batch_size=1)
str_to_print += '\n%s' % str(qval)
action = (np.argmax(qval)) #take action with highest Q-value
new_state = makeMove(state, action)
reward = getReward(new_state)
state = new_state
str_to_print += '\n%s' % 'Move #: %s; Taking action: %s' % (i, action)
str_to_print += '\n%s' % dispGrid(state)
if reward != -1 and reward != -5:
if reward == -10:
print(str_to_print + '\n%s' % "Reward: %s" % (reward, ))
return reward
print(str_to_print + '\n%s' % "Game lost; too many moves.")
return -10
def testAlgo(init=0):
arrow = ["^", "v", "<", ">"]
i = 0
if init==0:
state = initGrid()
elif init==1:
state = initGridPlayer()
else:
state = initGridRand()
drawgridworld.draw_state(state, 0)
time.sleep(0.5)
#print("Initial State:")
#print(dispGrid(state))
status = 1
#while game still in progress
while(status == 1):
qval = model.predict(state.reshape(1,64), batch_size=1)
action = (np.argmax(qval)) #take action with highest Q-value
print('Move #: %s; Taking action: %s' % (i, arrow[action]))
state = makeMove(state, action)
#print(dispGrid(state))
reward = getReward(state)
if reward != -1:
status = 0
print("Reward: %s" % (reward,))
i += 1 #If we're taking more than 10 actions, just stop, we probably can't win this game
drawgridworld.draw_state(state, i - 10)
time.sleep(0.5)
if (i > 10):
print("Game lost; too many moves.")
reward = -10
break
return reward
def testAlgo(init=0):
state = initGrid()
print("Initial State:")
dispGrid(state)
status = 1
i = 0
#while game still in progress
while (status == 1):
qval = model.predict(state.reshape(1, 80), batch_size=1)
action = (np.argmax(qval)) #take action with highest Q-value
print('Move #: %s; Taking action: %s' % (i, action))
state = makeMove(state, action, 4)
dispGrid(state)
reward = getReward(state)
if reward == -10:
print("The agent steped on the pit.. You won!")
state = 0
break
elif reward == 10:
print("The agent won!")
state = 0
break
print("Enter your move (0,1,2,3) for (up,down,left,right)")
action = int(input())
state = makeMove(state, action, 3)
reward = getReward(state, 3)
dispGrid(state)
if reward == -10:
print("You Lost!")
state = 0
break
elif reward == 10:
print("You won!")
state = 0
break
i += 1
#model.add(Dropout(0.2))
model.add(Dense(4, init='lecun_uniform'))
model.add(Activation(
'linear')) #linear output so we can have range of real-valued outputs
rms = RMSprop()
model.compile(loss='mse', optimizer=rms)
epochs = 1000
gamma = 0.9 #since it may take several moves to goal, making gamma high
epsilon = 1
for i in range(epochs):
state = initGrid()
status = 1
#while game still in progress
while (status == 1):
#We are in state S
#Let's run our Q function on S to get Q values for all possible actions
qval = model.predict(state.reshape(1, 80), batch_size=1)
if (random.random() < epsilon): #choose random action
action = np.random.randint(0, 4)
else: #choose best action from Q(s,a) values
action = (np.argmax(qval))
#Take action, observe new state S'
new_state = makeMove(state, action)
new_state = makeMove(new_state, np.random.randint(0, 4), 3)
#Observe reward
reward = getReward(new_state)