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:
pickle.dump(results, f)
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 time_steps.append(num_timesteps) env = get_new_environment(env_size, num_objects) game.env = env num_timesteps = 0 game.umpire.compare_agents_grammar(learners)