def agent_step(self, reward, observation):
action = None
self.window.erase()
self.window.addstr('STATE: %s\n' % (observation.intArray))
self.window.addstr('REWARD: %s\n' % (reward))
self.window.addstr('HIT UP, DOWN, LEFT or RIGHT to move...\n')
self.window.refresh()
try:
c = self.window.getch()
if c == curses.KEY_UP:
action = 'N'
elif c == curses.KEY_DOWN:
action = 'S'
elif c == curses.KEY_LEFT:
action = 'W'
elif c == curses.KEY_RIGHT:
action = 'E'
self.window.refresh()
except KeyboardInterrupt:
RLGlue.RL_cleanup()
a = Action()
if action:
a.charArray = [action]
return a
def do_step(self, state, reward = None):
"""
Runs the actual learning algorithm.
In a separate function so it can be called both on start and on step.
"""
#self.debug('do_step(', state, ',', reward, ')')
#if not state in self.Q:
# State not yet visited, initialize randomly
# self.Q[state] = self.random_actions()
# Run the Q update if this isn't the first step
action = None
if reward is not None:
action = self.update_Q(self.last_state, self.last_action, reward, state)
# Action object
a_obj = Action()
if action is None:
# Query the policy to find the best action
action = self.policy(state)
a_obj.charArray = list(action)
# Save the current state-action pair for the next step's Q update.
self.last_state = state
self.last_action = action
# And we're done
return a_obj
def agent_step(self, reward, observation):
action = None
self.window.erase()
self.window.addstr('STATE: %s\n' % (observation.intArray))
self.window.addstr('REWARD: %s\n' % (reward))
self.window.addstr('HIT UP, DOWN, LEFT or RIGHT to move...\n')
self.window.refresh()
try:
c = self.window.getch()
if c == curses.KEY_UP:
action = 'N'
elif c == curses.KEY_DOWN:
action = 'S'
elif c == curses.KEY_LEFT:
action = 'W'
elif c == curses.KEY_RIGHT:
action = 'E'
self.window.refresh()
except KeyboardInterrupt:
RLGlue.RL_cleanup()
a = Action()
if action:
a.charArray = [action]
return a
def do_step(self, state, reward=None):
"""
Runs the actual learning algorithm.
In a separate function so it can be called both on start and on step.
"""
#self.debug('do_step(', state, ',', reward, ')')
#if not state in self.Q:
# State not yet visited, initialize randomly
# self.Q[state] = self.random_actions()
# Run the Q update if this isn't the first step
action = None
if reward is not None:
action = self.update_Q(self.last_state, self.last_action, reward,
state)
# Action object
a_obj = Action()
if action is None:
# Query the policy to find the best action
action = self.policy(state)
a_obj.charArray = list(action)
# Save the current state-action pair for the next step's Q update.
self.last_state = state
self.last_action = action
# And we're done
return a_obj
def agent_step(self, reward, observation):
self.stepCount = self.stepCount + 1
action = Action()
action.intArray = observation.intArray
action.doubleArray = observation.doubleArray
action.charArray = observation.charArray
return action
def agent_start(self, observation):
self.stepCount = 0
action = Action()
action.intArray = observation.intArray
action.doubleArray = observation.doubleArray
action.charArray = observation.charArray
return action
def agent_step(self,reward, observation):
self.stepCount=self.stepCount+1
action=Action()
action.intArray=observation.intArray
action.doubleArray=observation.doubleArray
action.charArray=observation.charArray
return action
def agent_start(self,observation):
self.stepCount=0
action=Action()
action.intArray=observation.intArray
action.doubleArray=observation.doubleArray
action.charArray=observation.charArray
return action
def agent_step(self, reward, observation):
hypstr = ''.join(observation.charArray)
hyplist = hypstr.split('\t')
hyp_prob_list = [(hyplist[i], observation.doubleArray[i]) for i in range(len(hyplist))]
self.last_sys_act = self.dman.act_on(self.last_sys_act, hyp_prob_list)
self.dman.train(reward)
return_act = Action()
return_act.charArray = list(self.last_sys_act.encode('ascii', 'replace'))
return return_act
def agent_step(self, reward, observation):
hypstr = ''.join(observation.charArray)
hyplist = hypstr.split('\t')
hyp_prob_list = [(hyplist[i], observation.doubleArray[i])
for i in range(len(hyplist))]
self.last_sys_act = self.dman.act_on(self.last_sys_act,
hyp_prob_list)
self.dman.train(reward)
return_act = Action()
return_act.charArray = list(
self.last_sys_act.encode('ascii', 'replace'))
return return_act
def getAction(dir, isJump, isSpeed):
#-1, 0, 1 for direction, 1 is to the right
#0, 1 for jump
#0, 1 for speed
action = Action()
action.numInts = 3
action.numDoubles = 0
action.numChars = 0
action.intArray = []
action.doubleArray = []
action.charArray = []
action.intArray.append(dir)
action.intArray.append(isJump)
action.intArray.append(isSpeed)
return action
def do_step(self, state, reward=None):
"""
Runs the actual learning algorithm.
In a separate function so it can be called both on start and on step.
"""
#self.debug('do_step(', state, ',', reward, ')')
a_obj = Action()
# Query the policy to find the best action
action = self.policy(state)
a_obj.charArray = list(action)
# Run the Q update if this isn't the first step
if reward is not None:
self.update_Q(self.last_state, self.last_action, reward, state)
# Save the current state-action pair for the next step's Q update.
self.last_state = state
self.last_action = action
# And we're done
return a_obj
def do_step(self, state, reward = None):
"""
Runs the actual learning algorithm.
In a separate function so it can be called both on start and on step.
"""
#self.debug('do_step(', state, ',', reward, ')')
a_obj = Action()
# Query the policy to find the best action
action = self.policy(state)
a_obj.charArray = list(action)
# Run the Q update if this isn't the first step
if reward is not None:
self.update_Q(self.last_state, self.last_action, reward, state)
# Save the current state-action pair for the next step's Q update.
self.last_state = state
self.last_action = action
# And we're done
return a_obj
def do_step(self, state, reward = None):
""" Make an action from state, given an optional (previous) reward
In a separate function so it can be called both on start and on step.
"""
a_obj = Action()
# Query the policy to find the best action
action = self.policy(state)
a_obj.charArray = list(action)
#print 'action: ', action
# Run the parameter update if this isn't the first step
if reward is not None:
self.update_delta(tuple(self.last_state), tuple(self.last_action), reward)
# Save the current state-action pair for the next step update.
self.last_state = state
self.last_action = action
# Actionify!
return a_obj
def do_step(self, state, reward=None):
""" Make an action from state, given an optional (previous) reward
In a separate function so it can be called both on start and on step.
"""
a_obj = Action()
# Query the policy to find the best action
action = self.policy(state)
a_obj.charArray = list(action)
#print 'action: ', action
# Run the parameter update if this isn't the first step
if reward is not None:
self.update_delta(tuple(self.last_state), tuple(self.last_action),
reward)
# Save the current state-action pair for the next step update.
self.last_state = state
self.last_action = action
# Actionify!
return a_obj
