def hypertune(game):
recreate_directory_structure()
if not FLAGS.resume and FLAGS.train:
for i in range(FLAGS.nb_hyperparam_runs):
lr = 10**np.random.uniform(np.log10(10**(-2)), np.log10(
(10**(-4))))
gamma = np.random.uniform(0.7, 1.0)
model_name = "d_{}__lr_{}__gamma_{}".format(game, lr, gamma)
checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, model_name)
summaries_dir = os.path.join(FLAGS.summaries_dir, model_name)
frames_dir = os.path.join(FLAGS.frames_dir, model_name)
settings = {
"lr": lr,
"gamma": gamma,
"game": game,
"model_name": model_name,
"checkpoint_dir": checkpoint_dir,
"summaries_dir": summaries_dir,
"frames_dir": frames_dir
}
run(settings)
else:
model_instances = os.listdir(FLAGS.checkpoint_dir)
lrs = [inst.split("__")[1].split("_")[1] for inst in model_instances]
gammas = [
inst.split("__")[2].split("_")[1] for inst in model_instances
]
game = model_instances[0].split("__")[0].split("_")[1]
val_envs = TwoArms.get_envs(game, FLAGS.nb_test_episodes)
for i in range(len(model_instances)):
lr = lrs[i]
gamma = gammas[i]
model_name = "d_{}__lr_{}__gamma_{}".format(game, lr, gamma)
print(model_name)
checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, model_name)
summaries_dir = os.path.join(FLAGS.summaries_dir, model_name)
frames_dir = os.path.join(FLAGS.frames_dir, model_name)
settings = {
"lr": lr,
"gamma": gamma,
"game": game,
"model_name": model_name,
"checkpoint_dir": checkpoint_dir,
"summaries_dir": summaries_dir,
"frames_dir": frames_dir,
"envs": val_envs
}
run(settings)
def hypertune(game):
recreate_directory_structure()
if not FLAGS.resume and FLAGS.train:
for i in range(FLAGS.nb_hyperparam_runs):
lr = 10 ** np.random.uniform(np.log10(10 ** (-2)), np.log10((10 ** (-4))))
gamma = np.random.uniform(0.7, 1.0)
model_name = "d_{}__lr_{}__gamma_{}".format(game, lr, gamma)
checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, model_name)
summaries_dir = os.path.join(FLAGS.summaries_dir, model_name)
frames_dir = os.path.join(FLAGS.frames_dir, model_name)
settings = {"lr": lr,
"gamma": gamma,
"game": game,
"model_name": model_name,
"checkpoint_dir": checkpoint_dir,
"summaries_dir": summaries_dir,
"frames_dir": frames_dir}
run(settings)
else:
model_instances = os.listdir(FLAGS.checkpoint_dir)
lrs = [inst.split("__")[1].split("_")[1] for inst in model_instances]
gammas = [inst.split("__")[2].split("_")[1] for inst in model_instances]
game = model_instances[0].split("__")[0].split("_")[1]
val_envs = TwoArms.get_envs(game, FLAGS.nb_test_episodes)
for i in range(len(model_instances)):
lr = lrs[i]
gamma = gammas[i]
model_name = "d_{}__lr_{}__gamma_{}".format(game, lr, gamma)
print(model_name)
checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, model_name)
summaries_dir = os.path.join(FLAGS.summaries_dir, model_name)
frames_dir = os.path.join(FLAGS.frames_dir, model_name)
settings = {"lr": lr,
"gamma": gamma,
"game": game,
"model_name": model_name,
"checkpoint_dir": checkpoint_dir,
"summaries_dir": summaries_dir,
"frames_dir": frames_dir,
"envs": val_envs}
run(settings)
def validate_hypertune():
# recreate_directory_structure()
model_instances = os.listdir(FLAGS.checkpoint_dir)
lrs = [inst.split("__")[1].split("_")[1] for inst in model_instances]
gammas = [inst.split("__")[2].split("_")[1] for inst in model_instances]
game = model_instances[0].split("__")[0].split("_")[1]
val_envs = TwoArms.get_envs(game, FLAGS.nb_test_episodes)
with concurrent.futures.ThreadPoolExecutor(max_workers=multiprocessing.cpu_count()) as executor:
for i in range(len(model_instances)):
_ = executor.submit(thread_processing, lrs[i], gammas[i], val_envs, game)
def evaluate_one_test():
# test_envs = TwoArms.get_envs(FLAGS.game, FLAGS.nb_test_episodes)
#
# checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name)
# summaries_dir = os.path.join(FLAGS.summaries_dir, FLAGS.model_name)
# frames_dir = os.path.join(FLAGS.frames_dir, FLAGS.model_name)
#
# settings = {"lr": FLAGS.lr,
# "gamma": FLAGS.gamma,
# "game": FLAGS.game,
# "model_name": FLAGS.model_name,
# "checkpoint_dir": checkpoint_dir,
# "summaries_dir": summaries_dir,
# "frames_dir": frames_dir,
# "load_from": os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name),
# "envs": test_envs,
# "mode": "evaluate_once"}
#
# run(settings)
for _ in range(5):
model_name = "best_{}__lr_{}__gamma_{}".format(FLAGS.game, FLAGS.lr,
FLAGS.gamma)
load_from_model_name = "d_{}__lr_{}__gamma_{}".format(
FLAGS.best_model_game, FLAGS.lr, FLAGS.gamma)
print(load_from_model_name)
checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, model_name)
summaries_dir = os.path.join(FLAGS.summaries_dir, model_name)
frames_dir = os.path.join(FLAGS.frames_dir, model_name)
test_envs = TwoArms.get_envs(FLAGS.game, FLAGS.nb_test_episodes)
settings = {
"lr": FLAGS.lr,
"gamma": FLAGS.gamma,
"game": FLAGS.game,
"model_name": model_name,
"checkpoint_dir": checkpoint_dir,
"summaries_dir": summaries_dir,
"frames_dir": frames_dir,
"load_from": os.path.join(FLAGS.checkpoint_dir,
load_from_model_name),
"envs": test_envs,
"mode": "eval"
}
run(settings)
with open(FLAGS.results_eval_file, "r") as f:
line = f.readline()
mean_regrets = line.rstrip('\n').split(' ')
def test():
if tf.gfile.Exists(FLAGS.results_test_file):
os.remove(FLAGS.results_test_file)
with open(FLAGS.results_val_file, "r") as f:
content = f.readlines()
lines = [line.rstrip('\n') for line in content]
games = []
lrs = []
gammas = []
mean_regrets = []
mean_nb_subopt_armss = []
for line in lines:
results = line.split(" ")
results = results[1:]
game, lr, gamma, mean_regret, mean_nb_subopt_arms = [r.split("=")[1] for r in results]
games.append(game)
lrs.append(lr)
gammas.append(gamma)
mean_regrets.append(mean_regret)
mean_nb_subopt_armss.append(mean_nb_subopt_arms)
indices_best_n = np.asarray(mean_regrets).argsort()[-FLAGS.top:][::-1]
best_lrs = [lrs[i] for i in indices_best_n]
best_gammas = [gammas[i] for i in indices_best_n]
best_game = games[0]
test_envs = TwoArms.get_envs(best_game, FLAGS.nb_test_episodes)
for i in range(len(indices_best_n)):
model_name = "best_{}__lr_{}__gamma_{}".format(best_game, best_lrs[i], best_gammas[i])
load_from_model_name = "d_{}__lr_{}__gamma_{}".format(best_game, best_lrs[i], best_gammas[i])
print(model_name)
checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, model_name)
summaries_dir = os.path.join(FLAGS.summaries_dir, model_name)
frames_dir = os.path.join(FLAGS.frames_dir, model_name)
settings = {"lr": best_lrs[i],
"gamma": best_gammas[i],
"game": best_game,
"model_name": model_name,
"checkpoint_dir": checkpoint_dir,
"summaries_dir": summaries_dir,
"frames_dir": frames_dir,
"load_from": os.path.join(FLAGS.checkpoint_dir, load_from_model_name),
"envs": test_envs}
run(settings)
def evaluate_one_test():
# test_envs = TwoArms.get_envs(FLAGS.game, FLAGS.nb_test_episodes)
#
# checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name)
# summaries_dir = os.path.join(FLAGS.summaries_dir, FLAGS.model_name)
# frames_dir = os.path.join(FLAGS.frames_dir, FLAGS.model_name)
#
# settings = {"lr": FLAGS.lr,
# "gamma": FLAGS.gamma,
# "game": FLAGS.game,
# "model_name": FLAGS.model_name,
# "checkpoint_dir": checkpoint_dir,
# "summaries_dir": summaries_dir,
# "frames_dir": frames_dir,
# "load_from": os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name),
# "envs": test_envs,
# "mode": "evaluate_once"}
#
# run(settings)
for _ in range(5):
model_name = "best_{}__lr_{}__gamma_{}".format(FLAGS.game, FLAGS.lr, FLAGS.gamma)
load_from_model_name = "d_{}__lr_{}__gamma_{}".format(FLAGS.best_model_game, FLAGS.lr, FLAGS.gamma)
print(load_from_model_name)
checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, model_name)
summaries_dir = os.path.join(FLAGS.summaries_dir, model_name)
frames_dir = os.path.join(FLAGS.frames_dir, model_name)
test_envs = TwoArms.get_envs(FLAGS.game, FLAGS.nb_test_episodes)
settings = {"lr": FLAGS.lr,
"gamma": FLAGS.gamma,
"game": FLAGS.game,
"model_name": model_name,
"checkpoint_dir": checkpoint_dir,
"summaries_dir": summaries_dir,
"frames_dir": frames_dir,
"load_from": os.path.join(FLAGS.checkpoint_dir, load_from_model_name),
"envs": test_envs,
"mode": "eval"}
run(settings)
with open(FLAGS.results_eval_file, "r") as f:
line = f.readline()
mean_regrets = line.rstrip('\n').split(' ')
mean_regrets = [float(rg) for rg in mean_regrets]
mean_rg_avg = np.mean(mean_regrets)
print("Avg regret for the model is {}".format(mean_rg_avg))
def validate_hypertune():
# recreate_directory_structure()
model_instances = os.listdir(FLAGS.checkpoint_dir)
lrs = [inst.split("__")[1].split("_")[1] for inst in model_instances]
gammas = [inst.split("__")[2].split("_")[1] for inst in model_instances]
game = model_instances[0].split("__")[0].split("_")[1]
val_envs = TwoArms.get_envs(game, FLAGS.nb_test_episodes)
with concurrent.futures.ThreadPoolExecutor(
max_workers=multiprocessing.cpu_count()) as executor:
for i in range(len(model_instances)):
_ = executor.submit(thread_processing, lrs[i], gammas[i], val_envs,
game)
def run_one_test():
if FLAGS.train:
recreate_directory_structure()
model_name = "one_test"
checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, model_name)
summaries_dir = os.path.join(FLAGS.summaries_dir, model_name)
frames_dir = os.path.join(FLAGS.frames_dir, model_name)
settings = {
"lr": FLAGS.lr,
"gamma": FLAGS.gamma,
"game": FLAGS.game,
"model_name": model_name,
"checkpoint_dir": checkpoint_dir,
"summaries_dir": summaries_dir,
"frames_dir": frames_dir
}
else:
test_envs = TwoArms.get_envs(FLAGS.game, FLAGS.nb_test_episodes)
model_name = "one_test"
checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, model_name)
summaries_dir = os.path.join(FLAGS.summaries_dir, model_name)
frames_dir = os.path.join(FLAGS.frames_dir, model_name)
settings = {
"lr": FLAGS.lr,
"gamma": FLAGS.gamma,
"game": FLAGS.game,
"model_name": model_name,
"checkpoint_dir": checkpoint_dir,
"summaries_dir": summaries_dir,
"frames_dir": frames_dir,
"load_from": os.path.join(FLAGS.checkpoint_dir, model_name),
"envs": test_envs
}
run(settings)
def run_baseline():
test_envs = TwoArms.get_envs(FLAGS.game, FLAGS.nb_test_episodes)
model_name = "baseline"
settings = {
"model_name": model_name,
"game": FLAGS.game,
"envs": test_envs,
"exp_type": "evaluate_baseline"
}
tf.reset_default_graph()
with tf.device("/cpu:0"):
num_agents = 1
agents = []
envs = []
for i in range(num_agents):
if settings["game"] == '11arms':
this_env = ElevenArms()
else:
this_env = TwoArms(settings["game"])
envs.append(this_env)
for i in range(num_agents):
agents.append(RandomAgent(envs[i], i, settings))
with tf.Session() as sess:
coord = tf.train.Coordinator()
agent_threads = []
for agent in agents:
agent_play = lambda: agent.play(coord)
thread = threading.Thread(target=agent_play)
thread.start()
agent_threads.append(thread)
coord.join(agent_threads)
def run_baseline():
test_envs = TwoArms.get_envs(FLAGS.game, FLAGS.nb_test_episodes)
model_name = "baseline"
settings = {"model_name": model_name,
"game": FLAGS.game,
"envs": test_envs,
"exp_type": "evaluate_baseline"}
tf.reset_default_graph()
with tf.device("/cpu:0"):
num_agents = 1
agents = []
envs = []
for i in range(num_agents):
if settings["game"] == '11arms':
this_env = ElevenArms()
else:
this_env = TwoArms(settings["game"])
envs.append(this_env)
for i in range(num_agents):
agents.append(RandomAgent(envs[i], i, settings))
with tf.Session() as sess:
coord = tf.train.Coordinator()
agent_threads = []
for agent in agents:
agent_play = lambda: agent.play(coord)
thread = threading.Thread(target=agent_play)
thread.start()
agent_threads.append(thread)
coord.join(agent_threads)
def run_one_test():
if FLAGS.train:
recreate_directory_structure()
model_name = "one_test"
checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, model_name)
summaries_dir = os.path.join(FLAGS.summaries_dir, model_name)
frames_dir = os.path.join(FLAGS.frames_dir, model_name)
settings = {"lr": FLAGS.lr,
"gamma": FLAGS.gamma,
"game": FLAGS.game,
"model_name": model_name,
"checkpoint_dir": checkpoint_dir,
"summaries_dir": summaries_dir,
"frames_dir": frames_dir}
else:
test_envs = TwoArms.get_envs(FLAGS.game, FLAGS.nb_test_episodes)
model_name = "one_test"
checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, model_name)
summaries_dir = os.path.join(FLAGS.summaries_dir, model_name)
frames_dir = os.path.join(FLAGS.frames_dir, model_name)
settings = {"lr": FLAGS.lr,
"gamma": FLAGS.gamma,
"game": FLAGS.game,
"model_name": model_name,
"checkpoint_dir": checkpoint_dir,
"summaries_dir": summaries_dir,
"frames_dir": frames_dir,
"load_from": os.path.join(FLAGS.checkpoint_dir, model_name),
"envs": test_envs}
run(settings)
def test_hypertune():
if tf.gfile.Exists(FLAGS.results_test_file):
os.remove(FLAGS.results_test_file)
with open(FLAGS.results_val_file, "r") as f:
content = f.readlines()
lines = [line.rstrip('\n') for line in content]
games = []
lrs = []
gammas = []
mean_regrets = []
mean_nb_subopt_armss = []
for line in lines:
results = line.split(" ")
results = results[1:]
game, lr, gamma, mean_regret, mean_nb_subopt_arms = [
r.split("=")[1] for r in results
]
games.append(game)
lrs.append(lr)
gammas.append(gamma)
mean_regrets.append(float(mean_regret))
mean_nb_subopt_armss.append(float(mean_nb_subopt_arms))
indices_best_n = np.asarray(mean_regrets).argsort()[:FLAGS.top]
best_mean_regrests = [mean_regrets[i] for i in indices_best_n]
best_lrs = [lrs[i] for i in indices_best_n]
best_gammas = [gammas[i] for i in indices_best_n]
best_game = FLAGS.best_model_game
test_envs = TwoArms.get_envs(FLAGS.game, FLAGS.nb_test_episodes)
for i in range(len(indices_best_n)):
model_name = "best_{}__lr_{}__gamma_{}".format(
best_game, best_lrs[i], best_gammas[i])
load_from_model_name = "d_{}__lr_{}__gamma_{}".format(
best_game, best_lrs[i], best_gammas[i])
print(model_name)
checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, model_name)
summaries_dir = os.path.join(FLAGS.summaries_dir, model_name)
frames_dir = os.path.join(FLAGS.frames_dir, model_name)
settings = {
"lr":
best_lrs[i],
"gamma":
best_gammas[i],
"game":
FLAGS.game,
"model_name":
model_name,
"checkpoint_dir":
checkpoint_dir,
"summaries_dir":
summaries_dir,
"frames_dir":
frames_dir,
"load_from":
os.path.join(FLAGS.checkpoint_dir, load_from_model_name),
"envs":
test_envs,
"mode":
"test"
}
run(settings)