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Agent_5.py
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import random
import config
import utils
from prey import Prey
from predator import Predator
class Agent_5:
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
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 5 according to the given Agent 1 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
predator_certainty = 0.0
while game_count < number_of_games:
# Creating objects
prey = Prey()
predator = Predator()
agent5 = Agent_5(prey.curr_pos, predator.curr_pos)
step_count = 0
found_predator = True
believed_predator_curr_pos = predator.curr_pos
predator_certainty_counter = 0
while 1:
print("In game Agent_5 at game_count: ", game_count, " step_count: ", step_count)
# Survey a node initially without ever knowing where the prey is for a fact
found_predator, node_surveyed = utils.survey_predator(agent5, predator)
# prey belief state will be updated here
agent5.predator_belief_state = utils.update_predator_belief_state(agent5.predator_belief_state, \
agent5.curr_pos, \
agent5.prev_pos, \
arena, \
found_predator, \
node_surveyed, \
'after_survey')
if max(agent5.predator_belief_state.values()) == 1:
predator_certainty_counter += 1
believed_predator_curr_pos = utils.return_max_predator_belief(agent5.predator_belief_state, arena)
# using the max belief node for prey
agent5.move(arena, prey.curr_pos, believed_predator_curr_pos)
# Checking termination states
if agent5.curr_pos == prey.curr_pos:
win_count += 1
break
elif agent5.curr_pos == predator.curr_pos:
loss_count += 1
break
# update belief state
agent5.predator_belief_state = utils.update_predator_belief_state(agent5.predator_belief_state, \
agent5.curr_pos, \
agent5.prev_pos, \
arena, \
found_predator, \
node_surveyed, \
'after_agent_moves')
prey.move(arena)
# Checking termination states
if agent5.curr_pos == prey.curr_pos:
win_count += 1
break
predator.distracted_move(agent5.curr_pos, arena)
agent5.predator_belief_state = utils.update_predator_belief_state(agent5.predator_belief_state, \
agent5.curr_pos, \
agent5.prev_pos, \
arena, \
found_predator, \
node_surveyed, \
'after_predator_moves')
# Checking termination states
if agent5.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:
predator_certainty += predator_certainty_counter / step_count
else:
predator_certainty = 1.0
game_count += 1
data_row = ["Agent_5", win_count * 100 / number_of_games, loss_count * 100 / number_of_games,
forced_termination * 100 / number_of_games, 100.0, predator_certainty * 100 / number_of_games]
return data_row