def create_teams(strategy1, strategy2, max_tries_1=1, max_tries_2=1, num_agents=10):
agents1 = [
AgentWithStrategy(strategy1, architecture='matrix', creator='uniform_random', max_tries=max_tries_1)
for i in range(num_agents)]
agents2 = [
AgentWithStrategy(strategy2, architecture='matrix', creator='uniform_random', max_tries=max_tries_2)
for i in range(num_agents)]
return agents1, agents2
def create_game(neg_beta): env = GridEnvironment(env_size, env_size, goals=GOALS, symbols=AgentKnowledge.holistic_symbols) RandomInitializer(num_objects=num_objects, reduced=True).init_environments(env) learners = [AgentWithStrategy(StrategyTypes.MIXED, architecture=HolisticCognitiveArchitecture(len(AgentKnowledge.holistic_symbols), len(env.goals), neg_beta=neg_beta, random=True), role=Role.LISTENER, max_tries=num_tries, failure_alignment=True) for i in range(num_agents)] teacher = AgentWithStrategy(StrategyTypes.MIXED, architecture=HolisticCognitiveArchitecture(len(AgentKnowledge.holistic_symbols), len(env.goals)), role=Role.LISTENER) teacher.architecture.language_matrix = get_non_zero_permutation_matrix(len(AgentKnowledge.holistic_symbols), len(env.goals)) name = "teachers_learners_game_holistic_"+str(num_tries) game = CooperationHolisticGameWithTeachersAndLearners(env, [teacher], learners, name, umpire=HolisticGameUmpire(env, len(env.goals))) return game
def setup_game_with_new_agents(size, num_agents, num_objects):
env_1, env_2 = setup_environments(size, num_objects)
number_agents = num_agents
cooperative_agents = [
AgentWithStrategy(StrategyTypes.EXHAUSTIVE,
architecture='multiple',
creator='uniform_random')
for i in range(number_agents)
]
cooperative_game = CooperationGame(env_1, cooperative_agents,
"cooperative_10_agents_1")
non_cooperative_agents = [
AgentWithStrategy(StrategyTypes.NON_EXHAUSTIVE,
architecture='multiple',
creator='uniform_random')
for i in range(number_agents)
]
non_cooperative_game = CooperationGame(env_2, non_cooperative_agents,
"non_cooperative_10_agents_1")
return cooperative_game, non_cooperative_game
Agents keep a matrix representation of association between symbols and meanings
Word order is fixed: ACTION-COLOR-SHAPE-DIRECTION
"""
num_agents = 10
env_size = 8
num_objects = 60
num_games = 50
max_attempts = 20
if __name__ == "__main__":
results = []
for i in range(max_attempts):
print("group: ", i
agents = [AgentWithStrategy(StrategyTypes.MIXED, architecture='matrix', max_tries=i+1) for i in range(num_agents)]
grid_env = get_new_environment(env_size, num_objects)
game = CooperationGame(grid_env, agents, "cooperation-game-test", umpire="fixed_word_order")
is_game_finished = False
time_steps = []
for j in range(num_games):
print("game: ", j
num_timesteps = 0
while not is_game_finished:
is_game_finished = game.consume_time_step()
num_timesteps += 1
time_steps.append(num_timesteps)
game.set_new_environment(get_new_environment(env_size, num_objects))
is_game_finished = False
results.append((game.umpire, time_steps, agents))
with open('/Users/mariano/developing/repos/phdnotebooks/results/20_groups_umpires_fixed_order.pkl', 'w') as f:
num_games = 5
if __name__ == "__main__":
# agents = [AgentWithStrategy(StrategyTypes.MIXED, architecture='matrix_word_order', max_tries=5) for i in range(num_agents)]
# grid_env = GridEnvironment(env_size, env_size)
# RandomInitializer(num_objects=num_objects).init_environments(grid_env)
# game = CooperationGameWithWordOrder(grid_env, agents, "One_game_test")
# is_game_finished = False
# while not is_game_finished:
# is_game_finished = game.consume_time_step()
results = []
for i in range(max_attempts):
agents = [
AgentWithStrategy(StrategyTypes.MIXED,
architecture='matrix_variable_word_order',
max_tries=i + 1) for i in range(num_agents)
]
grid_env = get_new_environment(env_size, num_objects)
game = CooperationGameWithWordOrder(grid_env, agents,
"cooperation-game-test")
is_game_finished = False
time_steps = []
for j in range(num_games):
num_timesteps = 0
while not is_game_finished:
is_game_finished = game.consume_time_step()
num_timesteps += 1
time_steps.append(num_timesteps)
game.set_new_environment(get_new_environment(
env_size, num_objects))
from strategies import StrategyTypes from game_utils import get_new_environment, get_stochastic_permutation_matrix from environment import meanings, Categories num_agents = 20 num_games = 5 env_size = 8 num_objects = 60 if __name__ == "__main__": learners = [AgentWithStrategy(StrategyTypes.EXHAUSTIVE, architecture='counting_variable_word_order', role=Role.LISTENER) for i in range(num_agents/2)] teachers = [AgentWithStrategy(StrategyTypes.EXHAUSTIVE, architecture='matrix_3D_fixed_word_order', role=Role.SPEAKER) for i in range(num_agents/2)] language_matrix = get_stochastic_permutation_matrix(len(AgentKnowledge.symbols), len(meanings), Categories.get_number_of_categories()) for teacher in teachers: teacher.architecture.language_matrix = language_matrix env = get_new_environment(env_size, num_objects) game = CooperationGameWithTeachersAndLearners(env, teachers, learners, "teachers_learners_game") time_steps = [] num_timesteps = 0 is_game_finished = False for i in range(num_games): while not is_game_finished: if game.status.number_of_attempts == 6: pass is_game_finished = game.consume_time_step() num_timesteps += 1
return perform_interaction(game)
if __name__ == "__main__":
grid_env_1 = GridEnvironment(8, 8)
grid_env_2 = GridEnvironment(8, 8)
initializer = RandomInitializer(num_objects=100)
initializer.init_environments(grid_env_1, grid_env_2)
app = QtGui.QApplication([])
window = DoubleWindow(grid_env_1, grid_env_2, interval=.1)
number_agents = 10
cooperative_agents = [
AgentWithStrategy(StrategyTypes.EXHAUSTIVE,
architecture='multiple',
creator='uniform_random')
for i in range(number_agents)
]
cooperative_game = GraphicalGame(window.gridWidget_1, coop_interaction_cb,
cooperative_agents,
"cooperative_10_agents_1")
non_cooperative_agents = [
AgentWithStrategy(StrategyTypes.NON_EXHAUSTIVE,
architecture='multiple',
creator='uniform_random')
for i in range(number_agents)
]
non_cooperative_game = GraphicalGame(window.gridWidget_2,
non_coop_interaction_cb,
non_cooperative_agents,
from PyQt4 import QtGui
if __name__ == "__main__":
grid_env_1, grid_env_2 = setup_environments(8, 50)
app = QtGui.QApplication([])
name1 = "NON_EXHAUSTIVE_10_AGENTS_WORD_ORDER"
name2 = "EXHAUSTIVE_10_AGENTS_WORD_ORDER"
window = DoubleWindow(grid_env_1,
grid_env_2,
label1=name1,
label2=name2,
interval=0.5)
agents1 = [
AgentWithStrategy(StrategyTypes.NON_EXHAUSTIVE,
architecture='matrix_word_order') for i in range(10)
]
agents2 = [
AgentWithStrategy(StrategyTypes.EXHAUSTIVE,
architecture='matrix_word_order') for i in range(10)
]
game_1 = GraphicalGameWithStatus(window.gridWidget_1, agents1, name1)
game_2 = GraphicalGameWithStatus(window.gridWidget_2, agents2, name2)
window.timer.timeout.connect(game_1.timer_callback)
window.timer.timeout.connect(game_2.timer_callback)
window.show()
app.exec_()
from strategies import StrategyTypes
import pickle as pickle
from game_utils import get_new_environment
num_agents = 5
env_size = 8
num_objects = 40
num_games = 50
max_attempts = 20
if __name__ == "__main__":
results = []
for i in range(max_attempts):
print(i)
agents = [AgentWithStrategy(StrategyTypes.MIXED, architecture='multiple', max_tries=i+1, creator='uniform_random') for i in range(num_agents)]
grid_env = get_new_environment(env_size, num_objects)
game = CooperationGame(grid_env, agents, "cooperation-game-test")
is_game_finished = False
time_steps = []
for j in range(num_games):
print(j
num_timesteps = 0
while not is_game_finished:
is_game_finished = game.consume_time_step()
num_timesteps += 1
time_steps.append(num_timesteps)
game.set_new_environment(get_new_environment(env_size, num_objects))
is_game_finished = False
results.append((game.umpire, time_steps, agents))
with open('/Users/mariano/developing/repos/phdnotebooks/results/20_groups_umpires_holophrastic.pkl', 'w') as f:
return costs
if __name__ == "__main__":
num_agents = 10
num_objects = 50
num_games = 100
size = 8
reward = 300
results = []
costs = []
strat1 = StrategyTypes.EXHAUSTIVE
strat2 = StrategyTypes.MIXED
cooperative_agents = [
AgentWithStrategy(StrategyTypes.EXHAUSTIVE,
architecture='multiple',
creator='uniform_random') for i in range(num_agents)
]
non_cooperative_agents = [
AgentWithStrategy(StrategyTypes.MIXED,
architecture='multiple',
creator='uniform_random',
max_tries=2) for i in range(num_agents)
]
for i in range(num_games):
number_of_time_steps = 0
coop_game, not_coop_game = setup_game_with_agents(
size, num_objects, cooperative_agents, non_cooperative_agents)
is_game_finished = False
while not is_game_finished:
return False def coop_interaction_cb(game): return perform_interaction(game, True) def non_coop_interaction_cb(game): return perform_interaction(game, False) if __name__ == "__main__": grid_env_1 = GridEnvironment(8, 8) grid_env_2 = GridEnvironment(8, 8) initializer = RandomInitializer(num_objects=100) initializer.init_environments(grid_env_1, grid_env_2) app = QtGui.QApplication([]) window = DoubleWindow(grid_env_1, grid_env_2, interval=2.5) number_agents = 10 cooperative_agents = [AgentWithStrategy(StrategyTypes.EXHAUSTIVE) for i in range(number_agents)] cooperative_game = GraphicalGame(window.gridWidget_1, coop_interaction_cb, cooperative_agents, "cooperative_10_agents_1") non_cooperative_agents = [AgentWithStrategy(StrategyTypes.NON_EXHAUSTIVE) for i in range(number_agents)] non_cooperative_game = GraphicalGame(window.gridWidget_2, non_coop_interaction_cb, non_cooperative_agents, "non_cooperative_10_agents_1") window.timer.timeout.connect(cooperative_game.timer_callback) window.timer.timeout.connect(non_cooperative_game.timer_callback) window.show() app.exec_()
from agent import AgentWithStrategy, AgentKnowledge, HolisticCognitiveArchitecture
from game import CooperationHolisticGameWithTeachersAndLearners, Role, HolisticGameUmpire
from strategies import StrategyTypes
from game_utils import get_new_reduced_environment, get_permutation_matrix
import numpy as np
num_agents = 20
num_games = 30
env_size = 8
num_objects = 60
if __name__ == "__main__":
env = get_new_reduced_environment(env_size, num_objects)
learners = [AgentWithStrategy(StrategyTypes.EXHAUSTIVE, architecture=HolisticCognitiveArchitecture(len(AgentKnowledge.symbols), len(env.goals)), role=Role.LISTENER) for i in range(num_agents/2)]
teachers = [AgentWithStrategy(StrategyTypes.EXHAUSTIVE, architecture=HolisticCognitiveArchitecture(len(AgentKnowledge.symbols), len(env.goals)), role=Role.SPEAKER) for i in range(num_agents/2)]
language_matrix = get_permutation_matrix(len(AgentKnowledge.symbols), len(env.goals), float)
for teacher in teachers:
teacher.architecture.language_matrix = language_matrix
game = CooperationHolisticGameWithTeachersAndLearners(env, teachers, learners, "teachers_learners_game_holistic", umpire=HolisticGameUmpire(env, len(env.goals)))
is_game_finished = False
for i in range(num_games):
while not is_game_finished:
is_game_finished = game.consume_time_step()
env = get_new_reduced_environment(env_size, num_objects)
print("GAME FINISHED ", i
game.env = env
is_game_finished = False
for i in range(language_matrix.shape[0]):
