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Agent_7_wdd_handled.py
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import random
import config
import utils
from prey import Prey
from predator import Predator
class Agent_7_wdd_handled:
def __init__(self, prey_loc, predator_loc):
"""
Initializing the position of the Agent at locations where prey and predator are not present
Also initializes the belief state of the agent
Parameters:
self
prey_loc (int): Location of the prey
predator_loc (int): Location of the predator
"""
# Handling condition where prey and predator are spawned on the same location
list_to_choose_from = list(range(50))
if prey_loc == predator_loc:
list_to_choose_from.remove(prey_loc)
else:
list_to_choose_from.remove(prey_loc)
list_to_choose_from.remove(predator_loc)
self.curr_pos = random.choice(list_to_choose_from)
self.prev_pos = 999
# Initialize prey belief state
self.prey_belief_state = dict.fromkeys([i for i in range(50)], 1 / 49)
self.prey_belief_state[self.curr_pos] = 0
# Initialize peadator belief state
self.predator_belief_state = dict.fromkeys([i for i in range(50)], 0)
self.predator_belief_state[predator_loc] = 1
def move(self, arena, prey_loc, predator_loc):
"""
Moves Agent 1 according to the given priority
Parameters:
self
arena (dictionary): Adjacency list representing the graph
prey_loc (int): Location of prey
predator_loc (int): Location of Predator
"""
pos = utils.best_node(arena, self.curr_pos, prey_loc, predator_loc)
# Handling Sitting and praying case
if pos == 999:
pass
else:
self.prev_pos = self.curr_pos
self.curr_pos = pos
def begin(arena):
"""
Creates all the maze objects and plays number of games and collects data
Parameters:
arena (dict): Arena to use
Returns:
data_row (list): Results evaluated for the agent
"""
# Initiating game variables
game_count = 0
step_count = 0
# Initiating variables for analysis
win_count = 0
loss_count = 0
forced_termination = 0
data_row = []
number_of_games = config.NUMBER_OF_GAMES
forced_termination_threshold = config.FORCED_TERMINATION_THRESHOLD
prey_certainty = 0.0
predator_certainty = 0.0
while game_count < number_of_games:
# Creating objects
prey = Prey()
predator = Predator()
agent7_wdd_handled = Agent_7_wdd_handled(prey.curr_pos, predator.curr_pos)
step_count = 0
found_prey = False
found_predator = True
prey_certainty_counter = 0
predator_certainty_counter = 0
while 1:
print("In game Agent_7_wdd at game_count: ", game_count, " step_count: ", step_count)
print(agent7_wdd_handled.curr_pos, prey.curr_pos, predator.curr_pos)
# Check if it knows where the predator is
if max(agent7_wdd_handled.predator_belief_state.values()) == 1.0:
found_prey, node_surveyed = utils.survey_prey(agent7_wdd_handled, prey)
if found_prey:
if random.random() <= 0.1:
found_prey = False
else:
found_predator, node_surveyed = utils.survey_predator(agent7_wdd_handled, predator)
if found_predator:
if random.random() <= 0.1:
found_predator = False
# updating both belief states
agent7_wdd_handled.prey_belief_state = utils.update_prey_belief_state_defective_drone(agent7_wdd_handled.prey_belief_state, \
agent7_wdd_handled.curr_pos, \
agent7_wdd_handled.prev_pos, \
arena, \
found_prey, \
node_surveyed, \
'after_survey')
if max(agent7_wdd_handled.prey_belief_state.values()) == 1:
prey_certainty_counter += 1
agent7_wdd_handled.predator_belief_state = utils.update_predator_belief_state_defective_drone(agent7_wdd_handled.predator_belief_state, \
agent7_wdd_handled.curr_pos, \
agent7_wdd_handled.prev_pos, \
arena, \
found_predator, \
node_surveyed, \
'after_survey')
if max(agent7_wdd_handled.predator_belief_state.values()) == 1:
predator_certainty_counter += 1
believed_prey_curr_pos = utils.return_max_prey_belief(agent7_wdd_handled.prey_belief_state, arena)
believed_predator_curr_pos = utils.return_max_predator_belief(agent7_wdd_handled.predator_belief_state, arena)
# using the max belief node for prey
agent7_wdd_handled.move(arena, believed_prey_curr_pos, believed_predator_curr_pos)
# Checking termination states
if agent7_wdd_handled.curr_pos == prey.curr_pos:
win_count += 1
break
elif agent7_wdd_handled.curr_pos == predator.curr_pos:
loss_count += 1
break
# update belief state
agent7_wdd_handled.prey_belief_state = utils.update_prey_belief_state_defective_drone(agent7_wdd_handled.prey_belief_state, \
agent7_wdd_handled.curr_pos, \
agent7_wdd_handled.prev_pos, \
arena, \
found_prey, \
node_surveyed, \
'after_agent_moves')
agent7_wdd_handled.predator_belief_state = utils.update_predator_belief_state_defective_drone(agent7_wdd_handled.predator_belief_state, \
agent7_wdd_handled.curr_pos, \
agent7_wdd_handled.prev_pos, \
arena, \
found_predator, \
node_surveyed, \
'after_agent_moves')
prey.move(arena)
agent7_wdd_handled.prey_belief_state = utils.update_prey_belief_state_defective_drone(agent7_wdd_handled.prey_belief_state, \
agent7_wdd_handled.curr_pos, \
agent7_wdd_handled.prev_pos, \
arena, \
found_prey, \
node_surveyed, \
'after_prey_moves')
# Checking termination states
if agent7_wdd_handled.curr_pos == prey.curr_pos:
win_count += 1
break
predator.distracted_move(agent7_wdd_handled.curr_pos, arena)
agent7_wdd_handled.predator_belief_state = utils.update_predator_belief_state_defective_drone(agent7_wdd_handled.predator_belief_state, \
agent7_wdd_handled.curr_pos, \
agent7_wdd_handled.prev_pos, \
arena, \
found_predator, \
node_surveyed, \
'after_predator_moves')
# Checking termination states
if agent7_wdd_handled.curr_pos == predator.curr_pos:
loss_count += 1
break
step_count += 1
# Forcing termination
if step_count >= forced_termination_threshold:
forced_termination += 1
break
if step_count != 0:
prey_certainty += prey_certainty_counter / step_count
else:
prey_certainty = 0.0
if step_count != 0:
predator_certainty += predator_certainty_counter / step_count
else:
predator_certainty = 0.0
game_count += 1
data_row = ["Agent_7_wdd_handled", win_count * 100 / number_of_games, loss_count * 100 / number_of_games,
forced_termination * 100 / number_of_games, prey_certainty * 100 / number_of_games, predator_certainty * 100 / number_of_games]
return data_row