def set_available_actions(self, actions):
super(SLAgent, self).set_available_actions(actions)
# possible state values
state_n = len(self.preprocessor.enumerate_states())
self.nn = MLP(config='simple',
input_ranges=[[0, state_n]],
n_outputs=len(actions),
batch_size=4)
class SLAgent(Agent):
"""Agent using keras NN
"""
def __init__(self, n_frames_per_action=4):
super(SLAgent, self).__init__(name="SL", version="1")
self.experience = CircularList(1000)
self.epsilon = LinearInterpolationManager([(0, 1.0), (1e4, 0.1)])
self.action_repeat_manager = RepeatManager(n_frames_per_action - 1)
def select_action(self):
# Repeat last chosen action?
action = self.action_repeat_manager.next()
if action != None:
return action
state = self.preprocessor.process()
try:
s = np.array(state).reshape(len(state), 1)
except:
s = np.array(state).reshape(1, 1)
if self._sars[2]:
self._sars[3] = s
self.flush_experience()
# Consider postponing the first training until we have 32 samples
if len(self.experience) > 0:
self.nn.train(self.experience)
if np.random.random() < self.epsilon.next():
action = self.get_random_action()
else:
action_index = self.nn.predict(s)
action = self.available_actions[action_index]
self.action_repeat_manager.set(action)
self._sars[0] = s
self._sars[1] = self.available_actions.index(action)
return action
def set_available_actions(self, actions):
super(SLAgent, self).set_available_actions(actions)
# possible state values
state_n = len(self.preprocessor.enumerate_states())
self.nn = MLP(config="simple", input_ranges=[[0, state_n]], n_outputs=len(actions), batch_size=4)
def set_raw_state_callbacks(self, state_functions):
self.preprocessor = StateIndex(RelativeBall(state_functions, trinary=True))
def receive_reward(self, reward):
self._sars[2] = reward
def on_episode_start(self):
self._reset_sars()
def on_episode_end(self):
self._sars[3] = self._sars[0]
self._sars[4] = 0
self.flush_experience()
def flush_experience(self):
self.experience.append(tuple(self._sars))
self._reset_sars()
def _reset_sars(self):
# state, action, reward, newstate, newstate_not_terminal
self._sars = [None, None, None, None, 1]
def get_settings(self):
settings = {
"name": self.name,
"version": self.version,
"experience_replay": self.experience.capacity(),
"preprocessor": self.preprocessor.get_settings(),
"epsilon": self.epsilon.get_settings(),
"nn": self.nn.get_settings(),
}
settings.update(super(SLAgent, self).get_settings())
return settings
def set_available_actions(self, actions):
super(SLAgent, self).set_available_actions(actions)
# possible state values
state_n = len(self.preprocessor.enumerate_states())
self.nn = MLP(config="simple", input_ranges=[[0, state_n]], n_outputs=len(actions), batch_size=4)
class SLAgent(Agent):
"""Agent using keras NN
"""
def __init__(self, n_frames_per_action=4):
super(SLAgent, self).__init__(name='SL', version='1')
self.experience = CircularList(1000)
self.epsilon = LinearInterpolationManager([(0, 1.0), (1e4, 0.1)])
self.action_repeat_manager = RepeatManager(n_frames_per_action - 1)
def select_action(self):
# Repeat last chosen action?
action = self.action_repeat_manager.next()
if action != None:
return action
state = self.preprocessor.process()
try:
s = np.array(state).reshape(len(state), 1)
except:
s = np.array(state).reshape(1, 1)
if self._sars[2]:
self._sars[3] = s
self.flush_experience()
# Consider postponing the first training until we have 32 samples
if len(self.experience) > 0:
self.nn.train(self.experience)
if np.random.random() < self.epsilon.next():
action = self.get_random_action()
else:
action_index = self.nn.predict(s)
action = self.available_actions[action_index]
self.action_repeat_manager.set(action)
self._sars[0] = s
self._sars[1] = self.available_actions.index(action)
return action
def set_available_actions(self, actions):
super(SLAgent, self).set_available_actions(actions)
# possible state values
state_n = len(self.preprocessor.enumerate_states())
self.nn = MLP(config='simple',
input_ranges=[[0, state_n]],
n_outputs=len(actions),
batch_size=4)
def set_raw_state_callbacks(self, state_functions):
self.preprocessor = StateIndex(
RelativeBall(state_functions, trinary=True))
def receive_reward(self, reward):
self._sars[2] = reward
def on_episode_start(self):
self._reset_sars()
def on_episode_end(self):
self._sars[3] = self._sars[0]
self._sars[4] = 0
self.flush_experience()
def flush_experience(self):
self.experience.append(tuple(self._sars))
self._reset_sars()
def _reset_sars(self):
# state, action, reward, newstate, newstate_not_terminal
self._sars = [None, None, None, None, 1]
def get_settings(self):
settings = {
"name": self.name,
"version": self.version,
"experience_replay": self.experience.capacity(),
"preprocessor": self.preprocessor.get_settings(),
"epsilon": self.epsilon.get_settings(),
"nn": self.nn.get_settings(),
}
settings.update(super(SLAgent, self).get_settings())
return settings