def run(q, threadid):
random_instance = random.Random(100 + threadid)
random.seed(100 + threadid)
np.random.seed(100 + threadid)
num_agents = 4
# adhoc = AdhocAgent(3, mcts_c=mcts_c, mcts_k=mcts_k, mcts_n=mcts_n, behavior_model_size=bsize,
# environment_model_size=esize)
adhoc = AdhocAfterNAgent(agent_type(3),
episodes - 1,
3,
mcts_c=mcts_c,
mcts_k=mcts_k,
mcts_n=mcts_n,
behavior_model_size=bsize,
environment_model_size=esize)
agents = [agent_type(i) for i in range(num_agents - 1)] + [adhoc]
transition_f = get_transition_function(num_agents, world_size,
random.Random(100))
reward_f = get_reward_function(num_agents, world_size)
world = World(
PursuitState.random_state(num_agents, world_size, random_instance),
agents, transition_f, reward_f)
results = []
bmodelmetric = []
emodelmetric = []
emodelmetric_prey = []
try:
for i in range(episodes):
world.initial_state = PursuitState.random_state(
num_agents, world_size, random_instance)
timesteps, reward = world.run(0, 200)
results.append(timesteps)
timesteps = max(1, timesteps)
bmodelmetric.append(
sum(adhoc.b_model.metric[-timesteps:]) / timesteps)
emodelmetric.append(
sum(adhoc.e_model.metric[-timesteps:]) / timesteps)
emodelmetric_prey.append(
sum(adhoc.e_model.metric_prey[-timesteps:]) / timesteps)
q.put(1)
finally:
np.save(str(results_folder / 'results_{}'.format(threadid)),
np.array(results))
np.save(str(results_folder / 'eaccuracy_{}'.format(threadid)),
np.array(emodelmetric))
np.save(str(results_folder / 'baccuracy_{}'.format(threadid)),
np.array(bmodelmetric))
np.save(str(results_folder / 'eaccuracyprey_{}'.format(threadid)),
np.array(emodelmetric_prey))
def save_run(filename, number_episodes, agents, world_size=(5, 5), seed=100):
random_instance = random.Random(seed)
num_agents = len(agents)
transition_f = get_transition_function(num_agents, world_size, random.Random(seed))
reward_f = get_reward_function(num_agents, world_size)
transition_recorder = TransitionRecorder()
world = World(PursuitState.random_state(num_agents, world_size, random_instance), agents, transition_f, reward_f,
visualizers=(transition_recorder, ))
for i in range(number_episodes):
world.initial_state = PursuitState.random_state(num_agents, world_size, random_instance)
_, _ = world.run(0, 1000)
output_file = open(filename, 'wb')
pickle.dump(transition_recorder.transitions, output_file)
output_file.close()
def init(episodes, world_q):
random_instance = random.Random(100)
random.seed(100)
np.random.seed(100)
num_agents = 4
adhoc = AdhocAfterNAgent(agent_type(3), episodes, 3,
mcts_c=mcts_c, mcts_k=mcts_k, mcts_n=mcts_n, behavior_model_size=bsize,
environment_model_size=esize)
agents = [agent_type(i) for i in range(num_agents - 1)] + [adhoc]
transition_f = get_transition_function(num_agents, world_size, random.Random(100))
reward_f = get_reward_function(num_agents, world_size)
world = World(PursuitState.random_state(num_agents, world_size, random_instance), agents, transition_f, reward_f)
for _ in tqdm.tqdm(range(episodes)):
world.initial_state = PursuitState.random_state(num_agents, world_size, random_instance)
world.run(0, 200)
for _ in range(n_threads):
world_q.put(world)
return world, adhoc
mcts_c=mcts_c,
mcts_k=mcts_k,
mcts_n=mcts_n,
behavior_model_size=bsize,
environment_model_size=esize,
eps=1.0,
fit=None)
# adhoc = AdhocAgent.load('adhoc_dataset/10x10greedy_random_200')
agents = [agent_type(i) for i in range(num_agents - 1)] + [adhoc]
transition_f = get_transition_function(num_agents, world_size,
random.Random(100))
reward_f = get_reward_function(num_agents, world_size)
world = World(
PursuitState.random_state(num_agents, world_size, random_instance), agents,
transition_f, reward_f)
save_episodes = (1, 5, 10, 20, 50, 100, 150, 200)
current_episode = 0
for episodes in save_episodes:
for current_episode in range(current_episode, episodes):
world.initial_state = PursuitState.random_state(
num_agents, world_size, random_instance)
timesteps, reward = world.run(0, 100)
print(timesteps)
print("acc average " + str(np.average(adhoc.e_model.metric)))
print("acc prey average " + str(np.average(adhoc.e_model.metric_prey)))
print("behavior average " + str(np.average(adhoc.b_model.metric)))
adhoc.save(str(folder / ('10x10greedy_random_epochs5_' + str(episodes))))
