def get_action(self, state):
"""Get action from student's agent and compare with reference.
Returns an optimal action from reference.
"""
GameState.get_and_reset_explored()
student_action = (self.student_agent.get_action(state),
len(GameState.get_and_reset_explored()))
optimal_actions = self.optimal_actions[self.step_count]
alt_depth_actions = self.alt_depth_actions[self.step_count]
partial_ply_bug_actions = self.partial_ply_bug_actions[self.step_count]
student_optimal_action = False
current_right_states_explored = False
for i in range(len(optimal_actions)):
if student_action[0] in optimal_actions[i][0]:
student_optimal_action = True
else:
self.actions_consistent_with_optimal[i] = False
if student_action[1] == int(optimal_actions[i][1]):
current_right_states_explored = True
if (not current_right_states_explored
and self.wrong_states_explored < 0):
self.wrong_states_explored = 1
for i in range(len(alt_depth_actions)):
if student_action[0] not in alt_depth_actions[i]:
self.actions_consistent_with_alternative_depth[i] = False
for i in range(len(partial_ply_bug_actions)):
if student_action[0] not in partial_ply_bug_actions[i]:
self.actions_consistent_with_partial_ply_bug[i] = False
if not student_optimal_action:
self.suboptimal_moves.append(
(state, student_action[0], optimal_actions[0][0][0]))
self.step_count += 1
random.seed(self.seed + self.step_count)
return optimal_actions[0][0][0]
def get_action(self, state):
# survey agents
GameState.get_and_reset_explored()
optimal_action_lists = []
for agent in self.solution_agents:
optimal_action_lists.append(
(agent.get_best_pacman_actions(state)[0],
len(GameState.get_and_reset_explored())))
alternative_depth_lists = [
agent.get_best_pacman_actions(state)[0]
for agent in self.alternative_depth_agents
]
partial_ply_bug_lists = [
agent.get_best_pacman_actions(state)[0]
for agent in self.partial_ply_bug_agents
]
# record responses
self.optimal_action_lists.append(optimal_action_lists)
self.alternative_depth_lists.append(alternative_depth_lists)
self.partial_ply_bug_lists.append(partial_ply_bug_lists)
self.step_count += 1
random.seed(self.seed + self.step_count)
return optimal_action_lists[0][0][0]
