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
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