def main(config, out_dir):
if out_dir is not None:
tlogger.set_log_dir(out_dir)
log_dir = tlogger.log_dir()
if not os.path.exists(log_dir):
os.makedirs(log_dir)
tlogger.info(json.dumps(config, indent=4, sort_keys=True))
tlogger.info('Logging to: {}'.format(log_dir))
Model = neuroevolution.models.__dict__[config['model']]
all_tstart = time.time()
def make_env(b):
tlogger.info('GA: Creating environment for game: %s' % config["game"])
return gym_tensorflow.make(game=config["game"], batch_size=b)
tlogger.info('GA: Creating Concurent Workers')
worker = ConcurrentWorkers(make_env, Model, batch_size=64)
tlogger.info('GA: Concurent Workers Created')
with WorkerSession(worker) as sess:
noise = SharedNoiseTable()
rs = np.random.RandomState()
cached_parents = []
results = []
def make_offspring():
if len(cached_parents) == 0:
return worker.model.randomize(rs, noise)
else:
assert len(cached_parents) == config['selection_threshold']
parent = cached_parents[rs.randint(len(cached_parents))]
theta, seeds = worker.model.mutate(parent,
rs,
noise,
mutation_power=state.sample(
state.mutation_power))
#print("tetha len: %d, seeds len: %d" % (len(theta), len(seeds)))
return theta, seeds
tlogger.info('GA: Start timing')
tstart = time.time()
try:
load_file = os.path.join(log_dir, 'snapshot.pkl')
with open(load_file, 'rb+') as file:
state = pickle.load(file)
tlogger.info("Loaded iteration {} from {}".format(
state.it, load_file))
except FileNotFoundError:
tlogger.info('Failed to load snapshot')
state = TrainingState(config)
if 'load_population' in config:
tlogger.info('Loading population')
state.copy_population(config['load_population'])
# Cache first population if needed (on restart)
if state.population and config['selection_threshold'] > 0:
tlogger.info("Caching parents")
cached_parents.clear()
if state.elite in state.population[:config['selection_threshold']]:
cached_parents.extend([
(worker.model.compute_weights_from_seeds(noise,
o.seeds), o.seeds)
for o in state.population[:config['selection_threshold']]
])
else:
cached_parents.append((worker.model.compute_weights_from_seeds(
noise, state.elite.seeds), state.elite.seeds))
cached_parents.extend([
(worker.model.compute_weights_from_seeds(noise,
o.seeds), o.seeds)
for o in state.population[:config['selection_threshold'] -
1]
])
tlogger.info("Done caching parents")
while True:
tstart_iteration = time.time()
if state.timesteps_so_far >= config['timesteps']:
tlogger.info('Training terminated after {} timesteps'.format(
state.timesteps_so_far))
break
frames_computed_so_far = sess.run(worker.steps_counter)
assert (len(cached_parents) == 0 and state.it == 0
) or len(cached_parents) == config['selection_threshold']
tasks = [
make_offspring() for _ in range(config['population_size'])
]
for seeds, episode_reward, episode_length in worker.monitor_eval(
tasks, max_frames=state.tslimit * 4):
results.append(
Offspring(seeds, [episode_reward], [episode_length]))
state.num_frames += sess.run(
worker.steps_counter) - frames_computed_so_far
state.it += 1
tlogger.record_tabular('Iteration', state.it)
tlogger.record_tabular('MutationPower',
state.sample(state.mutation_power))
# Trim unwanted results
results = results[:config['population_size']]
assert len(results) == config['population_size']
rewards = np.array([a.fitness for a in results])
population_timesteps = sum([a.training_steps for a in results])
state.population = sorted(results,
key=lambda x: x.fitness,
reverse=True)
tlogger.record_tabular('PopulationEpRewMax', np.max(rewards))
tlogger.record_tabular('PopulationEpRewMean', np.mean(rewards))
tlogger.record_tabular('PopulationEpCount', len(rewards))
tlogger.record_tabular('PopulationTimesteps', population_timesteps)
tlogger.record_tabular('NumSelectedIndividuals',
config['selection_threshold'])
tlogger.info('Evaluate population')
validation_population = state.population[:config[
'validation_threshold']]
if state.elite is not None:
validation_population = [state.elite
] + validation_population[:-1]
validation_tasks = [(worker.model.compute_weights_from_seeds(
noise, validation_population[x].seeds,
cache=cached_parents), validation_population[x].seeds)
for x in range(config['validation_threshold'])]
_, population_validation, population_validation_len = zip(
*worker.monitor_eval_repeated(
validation_tasks,
max_frames=state.tslimit * 4,
num_episodes=config['num_validation_episodes']))
population_validation = [np.mean(x) for x in population_validation]
population_validation_len = [
np.sum(x) for x in population_validation_len
]
time_elapsed_this_iter = time.time() - tstart_iteration
state.time_elapsed += time_elapsed_this_iter
population_elite_idx = np.argmax(population_validation)
state.elite = validation_population[population_elite_idx]
elite_theta = worker.model.compute_weights_from_seeds(
noise, state.elite.seeds, cache=cached_parents)
_, population_elite_evals, population_elite_evals_timesteps = worker.monitor_eval_repeated(
[(elite_theta, state.elite.seeds)],
max_frames=None,
num_episodes=config['num_test_episodes'])[0]
# Log Results
validation_timesteps = sum(population_validation_len)
timesteps_this_iter = population_timesteps + validation_timesteps
state.timesteps_so_far += timesteps_this_iter
state.validation_timesteps_so_far += validation_timesteps
# Log
tlogger.record_tabular(
'TruncatedPopulationRewMean',
np.mean([a.fitness for a in validation_population]))
tlogger.record_tabular('TruncatedPopulationValidationRewMean',
np.mean(population_validation))
tlogger.record_tabular('TruncatedPopulationEliteValidationRew',
np.max(population_validation))
tlogger.record_tabular("TruncatedPopulationEliteIndex",
population_elite_idx)
tlogger.record_tabular('TruncatedPopulationEliteSeeds',
state.elite.seeds)
tlogger.record_tabular('TruncatedPopulationEliteTestRewMean',
np.mean(population_elite_evals))
tlogger.record_tabular('TruncatedPopulationEliteTestEpCount',
len(population_elite_evals))
tlogger.record_tabular('TruncatedPopulationEliteTestEpLenSum',
np.sum(population_elite_evals_timesteps))
if np.mean(population_validation) > state.curr_solution_val:
state.curr_solution = state.elite.seeds
state.curr_solution_val = np.mean(population_validation)
state.curr_solution_test = np.mean(population_elite_evals)
tlogger.record_tabular('ValidationTimestepsThisIter',
validation_timesteps)
tlogger.record_tabular('ValidationTimestepsSoFar',
state.validation_timesteps_so_far)
tlogger.record_tabular('TimestepsThisIter', timesteps_this_iter)
tlogger.record_tabular(
'TimestepsPerSecondThisIter',
timesteps_this_iter / (time.time() - tstart_iteration))
tlogger.record_tabular('TimestepsComputed', state.num_frames)
tlogger.record_tabular('TimestepsSoFar', state.timesteps_so_far)
tlogger.record_tabular('TimeElapsedThisIter',
time_elapsed_this_iter)
tlogger.record_tabular('TimeElapsedThisIterTotal',
time.time() - tstart_iteration)
tlogger.record_tabular('TimeElapsed', state.time_elapsed)
tlogger.record_tabular('TimeElapsedTotal',
time.time() - all_tstart)
tlogger.dump_tabular()
tlogger.info('Current elite: {}'.format(state.elite.seeds))
fps = state.timesteps_so_far / (time.time() - tstart)
tlogger.info(
'Timesteps Per Second: {:.0f}. Elapsed: {:.2f}h ETA {:.2f}h'.
format(fps, (time.time() - all_tstart) / 3600,
(config['timesteps'] - state.timesteps_so_far) / fps /
3600))
if state.adaptive_tslimit:
if np.mean(
[a.training_steps >= state.tslimit
for a in results]) > state.incr_tslimit_threshold:
state.tslimit = min(
state.tslimit * state.tslimit_incr_ratio,
state.tslimit_max)
tlogger.info('Increased threshold to {}'.format(
state.tslimit))
os.makedirs(log_dir, exist_ok=True)
save_file = os.path.join(log_dir, 'snapshot.pkl')
with open(save_file, 'wb+') as file:
pickle.dump(state, file)
#copyfile(save_file, os.path.join(log_dir, 'snapshot_gen{:04d}.pkl'.format(state.it)))
tlogger.info("Saved iteration {} to {}".format(
state.it, save_file))
if state.timesteps_so_far >= config['timesteps']:
tlogger.info('Training terminated after {} timesteps'.format(
state.timesteps_so_far))
break
results.clear()
if config['selection_threshold'] > 0:
tlogger.info("Caching parents")
new_parents = []
if state.elite in state.population[:config[
'selection_threshold']]:
new_parents.extend([
(worker.model.compute_weights_from_seeds(
noise, o.seeds, cache=cached_parents), o.seeds) for
o in state.population[:config['selection_threshold']]
])
else:
new_parents.append(
(worker.model.compute_weights_from_seeds(
noise, state.elite.seeds,
cache=cached_parents), state.elite.seeds))
new_parents.extend([
(worker.model.compute_weights_from_seeds(
noise, o.seeds, cache=cached_parents), o.seeds)
for o in
state.population[:config['selection_threshold'] - 1]
])
cached_parents.clear()
cached_parents.extend(new_parents)
tlogger.info("Done caching parents")
return float(state.curr_solution_test), float(state.curr_solution_val)
def main(**exp):
log_dir = tlogger.log_dir()
tlogger.info(json.dumps(exp, indent=4, sort_keys=True))
tlogger.info('Logging to: {}'.format(log_dir))
Model = neuroevolution.models.__dict__[exp['model']]
all_tstart = time.time()
def make_env(b):
return gym_tensorflow.make(game=exp["game"], batch_size=b)
worker = ConcurrentWorkers(make_env, Model, batch_size=64)
with WorkerSession(worker) as sess:
noise = SharedNoiseTable()
rs = np.random.RandomState()
tlogger.info('Start timing')
tstart = time.time()
try:
load_file = os.path.join(log_dir, 'snapshot.pkl')
with open(load_file, 'rb+') as file:
state = pickle.load(file)
tlogger.info("Loaded iteration {} from {}".format(
state.it, load_file))
except FileNotFoundError:
tlogger.info('Failed to load snapshot')
state = TrainingState(exp)
if 'load_from' in exp:
dirname = os.path.join(os.path.dirname(__file__), '..',
'neuroevolution', 'ga_legacy.py')
load_from = exp['load_from'].format(**exp)
os.system('python {} {} seeds.pkl'.format(dirname, load_from))
with open('seeds.pkl', 'rb+') as file:
seeds = pickle.load(file)
state.set_theta(
worker.model.compute_weights_from_seeds(noise, seeds))
tlogger.info('Loaded initial theta from {}'.format(load_from))
else:
state.initialize(rs, noise, worker.model)
def make_offspring(state):
for i in range(exp['population_size'] // 2):
idx = noise.sample_index(rs, worker.model.num_params)
mutation_power = state.sample(state.mutation_power)
pos_theta = worker.model.compute_mutation(
noise, state.theta, idx, mutation_power)
yield (pos_theta, idx)
neg_theta = worker.model.compute_mutation(
noise, state.theta, idx, -mutation_power)
diff = (np.max(
np.abs((pos_theta + neg_theta) / 2 - state.theta)))
assert diff < 1e-5, 'Diff too large: {}'.format(diff)
yield (neg_theta, idx)
tlogger.info('Start training')
_, initial_performance, _ = worker.monitor_eval_repeated(
[(state.theta, 0)],
max_frames=None,
num_episodes=exp['num_test_episodes'])[0]
while True:
tstart_iteration = time.time()
if state.timesteps_so_far >= exp['timesteps']:
tlogger.info('Training terminated after {} timesteps'.format(
state.timesteps_so_far))
break
frames_computed_so_far = sess.run(worker.steps_counter)
tlogger.info('Evaluating perturbations')
iterator = iter(
worker.monitor_eval(make_offspring(state),
max_frames=state.tslimit * 4))
results = []
for pos_seeds, pos_reward, pos_length in iterator:
neg_seeds, neg_reward, neg_length = next(iterator)
assert pos_seeds == neg_seeds
results.append(
Offspring(pos_seeds, [pos_reward, neg_reward],
[pos_length, neg_length]))
state.num_frames += sess.run(
worker.steps_counter) - frames_computed_so_far
state.it += 1
tlogger.record_tabular('Iteration', state.it)
tlogger.record_tabular('MutationPower',
state.sample(state.mutation_power))
tlogger.record_tabular('TimestepLimitPerEpisode', state.tslimit)
# Trim unwanted results
results = results[:exp['population_size'] // 2]
assert len(results) == exp['population_size'] // 2
rewards = np.array([b for a in results for b in a.rewards])
results_timesteps = np.array([a.training_steps for a in results])
timesteps_this_iter = sum([a.training_steps for a in results])
state.timesteps_so_far += timesteps_this_iter
tlogger.record_tabular('PopulationEpRewMax', np.max(rewards))
tlogger.record_tabular('PopulationEpRewMean', np.mean(rewards))
tlogger.record_tabular('PopulationEpRewMedian', np.median(rewards))
tlogger.record_tabular('PopulationEpCount', len(rewards))
tlogger.record_tabular('PopulationTimesteps', timesteps_this_iter)
# Update Theta
returns_n2 = np.array([a.rewards for a in results])
noise_inds_n = [a.seeds for a in results]
if exp['return_proc_mode'] == 'centered_rank':
proc_returns_n2 = compute_centered_ranks(returns_n2)
else:
raise NotImplementedError(exp['return_proc_mode'])
# Compute and take step
g, count = batched_weighted_sum(
proc_returns_n2[:, 0] - proc_returns_n2[:, 1],
(noise.get(idx, worker.model.num_params)
for idx in noise_inds_n),
batch_size=500)
# NOTE: gradients are scaled by \theta
g /= returns_n2.size
assert g.shape == (
worker.model.num_params,
) and g.dtype == np.float32 and count == len(noise_inds_n)
update_ratio, state.theta = state.optimizer.update(-g +
exp['l2coeff'] *
state.theta)
time_elapsed_this_iter = time.time() - tstart_iteration
state.time_elapsed += time_elapsed_this_iter
tlogger.info('Evaluate elite')
_, test_evals, test_timesteps = worker.monitor_eval_repeated(
[(state.theta, 0)],
max_frames=None,
num_episodes=exp['num_test_episodes'])[0]
test_timesteps = sum(test_timesteps)
# Log Results
tlogger.record_tabular('TestRewMean', np.mean(test_evals))
tlogger.record_tabular('TestRewMedian', np.median(test_evals))
tlogger.record_tabular('TestEpCount', len(test_evals))
tlogger.record_tabular('TestEpLenSum', test_timesteps)
tlogger.record_tabular('InitialRewMax',
np.max(initial_performance))
tlogger.record_tabular('InitialRewMean',
np.mean(initial_performance))
tlogger.record_tabular('InitialRewMedian',
np.median(initial_performance))
tlogger.record_tabular('TimestepsThisIter', timesteps_this_iter)
tlogger.record_tabular(
'TimestepsPerSecondThisIter',
timesteps_this_iter / (time.time() - tstart_iteration))
tlogger.record_tabular('TimestepsComputed', state.num_frames)
tlogger.record_tabular('TimestepsSoFar', state.timesteps_so_far)
tlogger.record_tabular('TimeElapsedThisIter',
time_elapsed_this_iter)
tlogger.record_tabular('TimeElapsedThisIterTotal',
time.time() - tstart_iteration)
tlogger.record_tabular('TimeElapsed', state.time_elapsed)
tlogger.record_tabular('TimeElapsedTotal',
time.time() - all_tstart)
tlogger.dump_tabular()
fps = state.timesteps_so_far / (time.time() - tstart)
tlogger.info(
'Timesteps Per Second: {:.0f}. Elapsed: {:.2f}h ETA {:.2f}h'.
format(fps, (time.time() - all_tstart) / 3600,
(exp['timesteps'] - state.timesteps_so_far) / fps /
3600))
if state.adaptive_tslimit:
if np.mean(
[a.training_steps >= state.tslimit
for a in results]) > state.incr_tslimit_threshold:
state.tslimit = min(
state.tslimit * state.tslimit_incr_ratio,
state.tslimit_max)
tlogger.info('Increased threshold to {}'.format(
state.tslimit))
os.makedirs(log_dir, exist_ok=True)
save_file = os.path.join(log_dir, 'snapshot.pkl')
with open(save_file, 'wb+') as file:
pickle.dump(state, file)
#copyfile(save_file, os.path.join(log_dir, 'snapshot_gen{:04d}.pkl'.format(state.it)))
tlogger.info("Saved iteration {} to {}".format(
state.it, save_file))
if state.timesteps_so_far >= exp['timesteps']:
tlogger.info('Training terminated after {} timesteps'.format(
state.timesteps_so_far))
break
results.clear()
def main(**exp):
log_dir = tlogger.log_dir()
tlogger.info(json.dumps(exp, indent=4, sort_keys=True))
tlogger.info('Logging to: {}'.format(log_dir))
Model = neuroevolution.models.__dict__[exp['model']]
all_tstart = time.time()
noise = SharedNoiseTable()
rs = np.random.RandomState()
def make_env0(b):
return gym_tensorflow.make(game=exp["games"][0], batch_size=b)
def make_env1(b):
return gym_tensorflow.make(game=exp["games"][1], batch_size=b)
workers = [
ConcurrentWorkers(make_env0, Model, batch_size=64),
ConcurrentWorkers(make_env1, Model, batch_size=64)
]
saver = tf.train.Saver()
tlogger.info('Start timing')
tstart = time.time()
tf_sess = tf.Session()
tf_sess.run(tf.global_variables_initializer())
state = TrainingState(exp)
state.initialize(rs, noise, workers[0].model)
workers[0].initialize(tf_sess)
workers[1].initialize(tf_sess)
for iteration in range(exp['iterations']):
tlogger.info("BEGINNING ITERATION: {}".format(iteration))
##############
### GAME 0 ###
##############
worker = workers[0]
frames_computed_so_far = tf_sess.run(worker.steps_counter)
game0_results = []
game0_rewards = []
game0_episode_lengths = []
iterator = iter(
worker.monitor_eval(make_offspring(exp, noise, rs, worker, state),
max_frames=state.tslimit * 4))
for pos_seeds, pos_reward, pos_length in iterator:
neg_seeds, neg_reward, neg_length = next(iterator)
assert pos_seeds == neg_seeds
result = Offspring(pos_seeds, [pos_reward, neg_reward],
[pos_length, neg_length])
rewards = result.rewards
game0_results.append(result)
game0_rewards.append(rewards)
game0_episode_lengths.append(result.ep_len)
state.num_frames += tf_sess.run(
worker.steps_counter) - frames_computed_so_far
game0_returns_n2 = np.array([a.rewards for a in game0_results])
game0_noise_inds_n = [a.seeds for a in game0_results]
# tlogger.info("game0 rewards: {}".format(np.mean(game0_rewards)))
# tlogger.info("game0 eplens: {}".format(game0_episode_lengths))
save_pickle(iteration, log_dir, "game0_rewards", game0_rewards)
save_pickle(iteration, log_dir, "game0_episode_lengths",
game0_episode_lengths)
##############
### GAME 1 ###
##############
worker = workers[1]
frames_computed_so_far = tf_sess.run(worker.steps_counter)
game1_results = []
game1_rewards = []
game1_episode_lengths = []
seeds_vector = np.array(game0_noise_inds_n)
iterator = iter(
worker.monitor_eval(make_offspring(exp, noise, rs, worker, state,
seeds_vector),
max_frames=state.tslimit * 4))
for pos_seeds, pos_reward, pos_length in iterator:
neg_seeds, neg_reward, neg_length = next(iterator)
assert pos_seeds == neg_seeds
result = Offspring(pos_seeds, [pos_reward, neg_reward],
[pos_length, neg_length])
rewards = result.rewards
game1_results.append(result)
game1_rewards.append(rewards)
game1_episode_lengths.append(result.ep_len)
state.num_frames += tf_sess.run(
worker.steps_counter) - frames_computed_so_far
game1_returns_n2 = np.array([a.rewards for a in game1_results])
game1_noise_inds_n = [a.seeds for a in game1_results]
# tlogger.info("game1 rewards: {}".format(np.mean(game1_rewards)))
# tlogger.info("game1 eplens: {}".format(game0_episode_lengths))
save_pickle(iteration, log_dir, "game1_rewards", game1_rewards)
save_pickle(iteration, log_dir, "game1_episode_lengths",
game1_episode_lengths)
tlogger.info("Saving offsprings seeds")
save_pickle(iteration, log_dir, "offsprings_seeds", game1_noise_inds_n)
####################
### UPDATE THETA ###
####################
game_returns = [game0_returns_n2, game1_returns_n2]
proc_returns = obtain_proc_returns(exp['learn_option'], game_returns)
assert game0_noise_inds_n == game1_noise_inds_n
noise_inds_n = game0_noise_inds_n + game1_noise_inds_n # concatenate the two lists
# TOP 100 offspring
# dx = proc_returns[:, 0]
# dy = proc_returns[:, 1]
# dist_squared = (np.ones(dx.shape) - np.abs(dx))**2 + (np.ones(dy.shape) - np.abs(dy))**2
# top_n_rewards = dist_squared.argsort()[-100:][::-1]
# batched_weighted_indices = (noise.get(idx, worker.model.num_params) for idx in noise_inds_n)
# proc_returns = proc_returns[top_n_rewards, :]
# batched_weighted_args = {
# 'deltas': proc_returns[:, 0] - proc_returns[:, 1],
# 'indices': [myval for myidx, myval in enumerate(batched_weighted_indices) if myidx in top_n_rewards]
# }
# noise_inds_n = batched_weighted_args['indices']
# g, count = batched_weighted_sum(batched_weighted_args['deltas'], batched_weighted_args['indices'], batch_size=len(batched_weighted_args['deltas']))
# ALL offspring
g, count = batched_weighted_sum(
proc_returns[:, 0] - proc_returns[:, 1],
(noise.get(idx, worker.model.num_params) for idx in noise_inds_n),
batch_size=500)
# NOTE: gradients are scaled by \theta
returns_n2 = np.array([a.rewards for a in game0_results] +
[a.rewards for a in game1_results])
# Only if using top 100
# returns_n2 = returns_n2[top_n_rewards]
g /= returns_n2.size
assert g.shape == (
worker.model.num_params,
) and g.dtype == np.float32 and count == len(noise_inds_n)
update_ratio, state.theta = state.optimizer.update(-g +
exp['l2coeff'] *
state.theta)
save_pickle(iteration, log_dir, "state", state)
######################
### EVALUATE ELITE ###
######################
_, test_evals, test_timesteps = workers[0].monitor_eval_repeated(
[(state.theta, 0)],
max_frames=None,
num_episodes=exp['num_test_episodes'] // 2)[0]
tlogger.info("game0 elite: {}".format(np.mean(test_evals)))
save_pickle(iteration, log_dir, 'game0_elite', test_evals)
save_pickle(iteration, log_dir, 'game0_elite_timestemps',
test_timesteps)
_, test_evals, test_timesteps = workers[1].monitor_eval_repeated(
[(state.theta, 0)],
max_frames=None,
num_episodes=exp['num_test_episodes'] // 2)[0]
tlogger.info("game1 elite: {}".format(np.mean(test_evals)))
save_pickle(iteration, log_dir, "game1_elite", test_evals)
save_pickle(iteration, log_dir, 'game1_elite_timestemps',
test_timesteps)
state.num_frames += tf_sess.run(
worker.steps_counter) - frames_computed_so_far
saver.save(tf_sess, "{}/model-{}".format(log_dir, state.it))
state.it += 1
os.kill(os.getpid(), signal.SIGTERM)
def main(**exp):
log_dir = tlogger.log_dir()
tlogger.info(json.dumps(exp, indent=4, sort_keys=True))
tlogger.info('Logging to: {}'.format(log_dir))
Model = neuroevolution.models.__dict__[exp['model']]
all_tstart = time.time()
noise = SharedNoiseTable()
rs = np.random.RandomState()
def make_env0(b):
return gym_tensorflow.make(game=exp["games"][0], batch_size=b)
def make_env1(b):
return gym_tensorflow.make(game=exp["games"][1], batch_size=b)
workers = [
ConcurrentWorkers(make_env0, Model, batch_size=64),
ConcurrentWorkers(make_env1, Model, batch_size=64)
]
tlogger.info('Start timing')
tstart = time.time()
tf_sess = tf.Session()
tf_sess.run(tf.global_variables_initializer())
state = TrainingState(exp)
state.initialize(rs, noise, workers[0].model)
workers[0].initialize(tf_sess)
workers[1].initialize(tf_sess)
for iteration in range(exp['iterations']):
tlogger.info("BEGINNING ITERATION: {}".format(iteration))
##############
### GAME 0 ###
##############
worker = workers[0]
frames_computed_so_far = tf_sess.run(worker.steps_counter)
game0_results = []
game0_rewards = []
game0_episode_lengths = []
iterator = iter(
worker.monitor_eval(make_offspring(exp, noise, rs, worker, state),
max_frames=state.tslimit * 4))
for pos_seeds, pos_reward, pos_length in iterator:
neg_seeds, neg_reward, neg_length = next(iterator)
assert pos_seeds == neg_seeds
result = Offspring(pos_seeds, [pos_reward, neg_reward],
[pos_length, neg_length])
rewards = result.rewards
game0_results.append(result)
game0_rewards.append(rewards)
game0_episode_lengths.append(result.ep_len)
state.num_frames += tf_sess.run(
worker.steps_counter) - frames_computed_so_far
game0_returns_n2 = np.array([a.rewards for a in game0_results])
game0_noise_inds_n = [a.seeds for a in game0_results]
save_pickle(iteration, log_dir, "game0_rewards", game0_rewards)
save_pickle(iteration, log_dir, "game0_episode_lengths",
game0_episode_lengths)
##############
### GAME 1 ###
##############
if f_isSingleTask(exp):
game1_results = []
game1_rewards = []
game1_episode_lengths = []
game1_returns_n2 = game0_returns_n2
game1_noise_inds_n = game0_noise_inds_n
else:
worker = workers[1]
frames_computed_so_far = tf_sess.run(worker.steps_counter)
game1_results = []
game1_rewards = []
game1_episode_lengths = []
seeds_vector = np.array(game0_noise_inds_n)
iterator = iter(
worker.monitor_eval(make_offspring(exp, noise, rs, worker,
state, seeds_vector),
max_frames=state.tslimit * 4))
for pos_seeds, pos_reward, pos_length in iterator:
neg_seeds, neg_reward, neg_length = next(iterator)
assert pos_seeds == neg_seeds
result = Offspring(pos_seeds, [pos_reward, neg_reward],
[pos_length, neg_length])
rewards = result.rewards
game1_results.append(result)
game1_rewards.append(rewards)
game1_episode_lengths.append(result.ep_len)
state.num_frames += tf_sess.run(
worker.steps_counter) - frames_computed_so_far
game1_returns_n2 = np.array([a.rewards for a in game1_results])
game1_noise_inds_n = [a.seeds for a in game1_results]
save_pickle(iteration, log_dir, "game1_rewards", game1_rewards)
save_pickle(iteration, log_dir, "game1_episode_lengths",
game1_episode_lengths)
tlogger.info("Saving offsprings seeds")
save_pickle(iteration, log_dir, "offsprings_seeds", game1_noise_inds_n)
####################
### UPDATE THETA ###
####################
if f_isSingleTask(exp):
proc_frames = compute_centered_ranks(
np.asarray(game0_episode_lengths))
proc_returns = compute_centered_ranks(game0_returns_n2)
noise_inds_n = game0_noise_inds_n
else:
game_returns = [game0_returns_n2, game1_returns_n2]
proc_returns = obtain_proc_returns(exp['learn_option'],
game_returns)
assert game0_noise_inds_n == game1_noise_inds_n
noise_inds_n = game0_noise_inds_n + game1_noise_inds_n # concatenate the two lists
g_returns, count_returns = batched_weighted_sum(
proc_returns[:, 0] - proc_returns[:, 1],
(noise.get(idx, worker.model.num_params) for idx in noise_inds_n),
batch_size=500)
g_frames, count_frames = batched_weighted_sum(
proc_frames[:, 0] - proc_frames[:, 1],
(noise.get(idx, worker.model.num_params) for idx in noise_inds_n),
batch_size=500)
assert count_frames == count_returns
count = count_returns
w = exp['w']
g = w * g_returns + (1 - w) * g_frames
returns_n2 = np.array([a.rewards for a in game0_results] +
[a.rewards for a in game1_results])
g /= returns_n2.size
assert g.shape == (
worker.model.num_params,
) and g.dtype == np.float32 and count == len(noise_inds_n)
update_ratio, state.theta = state.optimizer.update(-g +
exp['l2coeff'] *
state.theta)
save_pickle(iteration, log_dir, "state", state)
######################
### EVALUATE ELITE ###
######################
_, test_evals, test_timesteps = workers[0].monitor_eval_repeated(
[(state.theta, 0)],
max_frames=None,
num_episodes=exp['num_test_episodes'] //
(2**(1 - f_isSingleTask(exp))))[0]
tlogger.info("game0 elite: {}".format(np.mean(test_evals)))
tlogger.info("game0 elite frames max: {}".format(
np.max(test_timesteps)))
tlogger.info("game0 elite frames mean: {}".format(
np.mean(test_timesteps)))
tlogger.info("game0 elite frames min: {}".format(
np.min(test_timesteps)))
tlogger.info("game0 offspring frames max: {}".format(
np.max(game0_episode_lengths)))
tlogger.info("game0 offspring frames mean: {}".format(
np.mean(game0_episode_lengths)))
tlogger.info("game0 offspring frames min: {}".format(
np.min(game0_episode_lengths)))
save_pickle(iteration, log_dir, 'game0_elite', test_evals)
save_pickle(iteration, log_dir, 'game0_elite_timestemps',
test_timesteps)
if not (f_isSingleTask(exp)):
_, test_evals, test_timesteps = workers[1].monitor_eval_repeated(
[(state.theta, 0)],
max_frames=None,
num_episodes=exp['num_test_episodes'] // 2)[0]
tlogger.info("game1 elite: {}".format(np.mean(test_evals)))
save_pickle(iteration, log_dir, "game1_elite", test_evals)
save_pickle(iteration, log_dir, 'game1_elite_timestemps',
test_timesteps)
state.num_frames += tf_sess.run(
worker.steps_counter) - frames_computed_so_far
state.it += 1
os.kill(os.getpid(), signal.SIGTERM)
def main(exp, log_dir):
log_dir = tlogger.log_dir(log_dir)
snap_idx = 0
snapshots = []
tlogger.info(json.dumps(exp, indent=4, sort_keys=True))
tlogger.info('Logging to: {}'.format(log_dir))
Model = neuroevolution.models.__dict__[exp['model']]
all_tstart = time.time()
def make_env(b):
return gym_tensorflow.make(game=exp["game"], batch_size=b)
worker = ConcurrentWorkers(make_env, Model, batch_size=64)
with WorkerSession(worker) as sess:
rs = np.random.RandomState()
noise = None
state = None
cached_parents = []
results = []
def make_offspring():
if len(cached_parents) == 0:
return worker.model.randomize(rs, noise)
else:
assert len(cached_parents) == exp['selection_threshold']
parent = cached_parents[rs.randint(len(cached_parents))]
return worker.model.mutate(parent,
rs,
noise,
mutation_power=state.sample(
state.mutation_power))
tlogger.info('Start timing')
tstart = time.time()
load_file = os.path.join(log_dir, 'snapshot.pkl')
if 'load_from' in exp:
filename = os.path.join(log_dir, exp['load_from'])
with open(filename, 'rb+') as file:
state = pickle.load(file)
state.timesteps_so_far = 0 # Reset timesteps to 0
state.it = 0
state.max_reward = 0
state.max_avg = 0
state.max_sd = 0
tlogger.info('Loaded initial policy from {}'.format(filename))
elif os.path.exists(load_file):
try:
with open(load_file, 'rb+') as file:
state = pickle.load(file)
tlogger.info("Loaded iteration {} from {}".format(
state.it, load_file))
except FileNotFoundError:
tlogger.info('Failed to load snapshot')
if not noise:
tlogger.info("Generating new noise table")
noise = SharedNoiseTable()
else:
tlogger.info("Using noise table from snapshot")
if not state:
tlogger.info("Generation new TrainingState")
state = TrainingState(exp)
if 'load_population' in exp:
state.copy_population(exp['load_population'])
# Cache first population if needed (on restart)
if state.population and exp['selection_threshold'] > 0:
tlogger.info("Caching parents")
cached_parents.clear()
if state.elite in state.population[:exp['selection_threshold']]:
cached_parents.extend([
(worker.model.compute_weights_from_seeds(noise,
o.seeds), o.seeds)
for o in state.population[:exp['selection_threshold']]
])
else:
cached_parents.append((worker.model.compute_weights_from_seeds(
noise, state.elite.seeds), state.elite.seeds))
cached_parents.extend([
(worker.model.compute_weights_from_seeds(noise,
o.seeds), o.seeds)
for o in state.population[:exp['selection_threshold'] - 1]
])
tlogger.info("Done caching parents")
while True:
tstart_iteration = time.time()
if state.timesteps_so_far >= exp['timesteps']:
tlogger.info('Training terminated after {} timesteps'.format(
state.timesteps_so_far))
break
frames_computed_so_far = sess.run(worker.steps_counter)
assert (len(cached_parents) == 0 and state.it
== 0) or len(cached_parents) == exp['selection_threshold']
tasks = [make_offspring() for _ in range(exp['population_size'])]
for seeds, episode_reward, episode_length in worker.monitor_eval(
tasks, max_frames=state.tslimit * 4):
results.append(
Offspring(seeds, [episode_reward], [episode_length]))
state.num_frames += sess.run(
worker.steps_counter) - frames_computed_so_far
state.it += 1
tlogger.record_tabular('Iteration', state.it)
tlogger.record_tabular('MutationPower',
state.sample(state.mutation_power))
# Trim unwanted results
results = results[:exp['population_size']]
assert len(results) == exp['population_size']
rewards = np.array([a.fitness for a in results])
population_timesteps = sum([a.training_steps for a in results])
state.population = sorted(results,
key=lambda x: x.fitness,
reverse=True)
state.max_reward = save_best_pop_member(state.max_reward,
np.max(rewards), state,
state.population[0])
tlogger.record_tabular('PopulationEpRewMax', np.max(rewards))
tlogger.record_tabular('PopulationEpRewMean', np.mean(rewards))
tlogger.record_tabular('PopulationEpCount', len(rewards))
tlogger.record_tabular('PopulationTimesteps', population_timesteps)
tlogger.record_tabular('NumSelectedIndividuals',
exp['selection_threshold'])
tlogger.info('Evaluate population')
validation_population = state.population[:exp[
'validation_threshold']]
if state.elite is not None:
validation_population = [state.elite
] + validation_population[:-1]
validation_tasks = [(worker.model.compute_weights_from_seeds(
noise, validation_population[x].seeds,
cache=cached_parents), validation_population[x].seeds)
for x in range(exp['validation_threshold'])]
_, population_validation, population_validation_len = zip(
*worker.monitor_eval_repeated(
validation_tasks,
max_frames=state.tslimit * 4,
num_episodes=exp['num_validation_episodes']))
it_max_avg = np.max([np.mean(x) for x in population_validation])
it_max_sd = np.max([np.std(x) for x in population_validation])
state.max_avg = np.max([state.max_avg, it_max_avg])
state.max_sd = np.max([state.max_sd, it_max_sd])
tlogger.info("Max Average: {}".format(state.max_avg))
tlogger.info("Max Std: {}".format(state.max_sd))
fitness_results = [(np.mean(x), np.std(x))
for x in population_validation]
with open(os.path.join(log_dir, 'fitness.log'), 'a') as f:
f.write("{},{},{}: {}\n".format(
state.it, state.max_avg, state.max_sd, ','.join([
"({},{})".format(x[0], x[1]) for x in fitness_results
])))
population_fitness = [
fitness(x[0], x[1], state.max_avg, state.max_sd)
for x in fitness_results
]
tlogger.info("Fitness: {}".format(population_fitness))
population_validation_len = [
np.sum(x) for x in population_validation_len
]
time_elapsed_this_iter = time.time() - tstart_iteration
state.time_elapsed += time_elapsed_this_iter
population_elite_idx = np.argmin(population_fitness)
state.elite = validation_population[population_elite_idx]
elite_theta = worker.model.compute_weights_from_seeds(
noise, state.elite.seeds, cache=cached_parents)
_, population_elite_evals, population_elite_evals_timesteps = worker.monitor_eval_repeated(
[(elite_theta, state.elite.seeds)],
max_frames=None,
num_episodes=exp['num_test_episodes'])[0]
# Log Results
validation_timesteps = sum(population_validation_len)
timesteps_this_iter = population_timesteps + validation_timesteps
state.timesteps_so_far += timesteps_this_iter
state.validation_timesteps_so_far += validation_timesteps
# Log
tlogger.record_tabular(
'TruncatedPopulationRewMean',
np.mean([a.fitness for a in validation_population]))
tlogger.record_tabular('TruncatedPopulationValidationFitMean',
np.mean(population_fitness))
tlogger.record_tabular('TruncatedPopulationValidationFitMax',
np.max(population_fitness))
tlogger.record_tabular('TruncatedPopulationValidationFitMin',
np.min(population_fitness))
tlogger.record_tabular('TruncatedPopulationValidationMaxAvg',
state.max_avg)
tlogger.record_tabular('TruncatedPopulationValidationMaxStd',
state.max_sd)
tlogger.record_tabular('TruncatedPopulationEliteValidationFitMin',
np.min(population_fitness))
tlogger.record_tabular("TruncatedPopulationEliteIndex",
population_elite_idx)
tlogger.record_tabular('TruncatedPopulationEliteSeeds',
state.elite.seeds)
tlogger.record_tabular('TruncatedPopulationEliteTestRewMean',
np.mean(population_elite_evals))
tlogger.record_tabular('TruncatedPopulationEliteTestRewStd',
np.std(population_elite_evals))
tlogger.record_tabular('TruncatedPopulationEliteTestEpCount',
len(population_elite_evals))
tlogger.record_tabular('TruncatedPopulationEliteTestEpLenSum',
np.sum(population_elite_evals_timesteps))
if np.mean(population_validation) > state.curr_solution_val:
state.curr_solution = state.elite.seeds
state.curr_solution_val = np.mean(population_validation)
state.curr_solution_test = np.mean(population_elite_evals)
tlogger.record_tabular('ValidationTimestepsThisIter',
validation_timesteps)
tlogger.record_tabular('ValidationTimestepsSoFar',
state.validation_timesteps_so_far)
tlogger.record_tabular('TimestepsThisIter', timesteps_this_iter)
tlogger.record_tabular(
'TimestepsPerSecondThisIter',
timesteps_this_iter / (time.time() - tstart_iteration))
tlogger.record_tabular('TimestepsComputed', state.num_frames)
tlogger.record_tabular('TimestepsSoFar', state.timesteps_so_far)
tlogger.record_tabular('TimeElapsedThisIter',
time_elapsed_this_iter)
tlogger.record_tabular('TimeElapsedThisIterTotal',
time.time() - tstart_iteration)
tlogger.record_tabular('TimeElapsed', state.time_elapsed)
tlogger.record_tabular('TimeElapsedTotal',
time.time() - all_tstart)
tlogger.dump_tabular()
# tlogger.info('Current elite: {}'.format(state.elite.seeds))
fps = state.timesteps_so_far / (time.time() - tstart)
tlogger.info(
'Timesteps Per Second: {:.0f}. Elapsed: {:.2f}h ETA {:.2f}h'.
format(fps, (time.time() - all_tstart) / 3600,
(exp['timesteps'] - state.timesteps_so_far) / fps /
3600))
if state.adaptive_tslimit:
if np.mean(
[a.training_steps >= state.tslimit
for a in results]) > state.incr_tslimit_threshold:
state.tslimit = min(
state.tslimit * state.tslimit_incr_ratio,
state.tslimit_max)
tlogger.info('Increased threshold to {}'.format(
state.tslimit))
snap_idx, snapshots = save_snapshot(state, log_dir, snap_idx,
snapshots)
# os.makedirs(log_dir, exist_ok=True)
# copyfile(save_file, os.path.join(log_dir, 'snapshot_gen{:04d}.pkl'.format(state.it)))
tlogger.info("Saved iteration {} to {}".format(
state.it, snapshots[snap_idx - 1]))
if state.timesteps_so_far >= exp['timesteps']:
tlogger.info('Training terminated after {} timesteps'.format(
state.timesteps_so_far))
break
results.clear()
if exp['selection_threshold'] > 0:
tlogger.info("Caching parents")
new_parents = []
if state.elite in state.population[:
exp['selection_threshold']]:
new_parents.extend([
(worker.model.compute_weights_from_seeds(
noise, o.seeds, cache=cached_parents), o.seeds)
for o in state.population[:exp['selection_threshold']]
])
else:
new_parents.append(
(worker.model.compute_weights_from_seeds(
noise, state.elite.seeds,
cache=cached_parents), state.elite.seeds))
new_parents.extend([
(worker.model.compute_weights_from_seeds(
noise, o.seeds, cache=cached_parents), o.seeds)
for o in state.population[:exp['selection_threshold'] -
1]
])
cached_parents.clear()
cached_parents.extend(new_parents)
tlogger.info("Done caching parents")
return float(state.curr_solution_test), {
'val': float(state.curr_solution_val)
}
def main(**exp):
log_dir = tlogger.log_dir()
tlogger.info(json.dumps(exp, indent=4, sort_keys=True))
tlogger.info('Logging to: {}'.format(log_dir))
Model = neuroevolution.models.__dict__[exp['model']]
all_tstart = time.time()
noise = SharedNoiseTable()
rs = np.random.RandomState()
def make_env0(b):
return gym_tensorflow.make(game=exp["games"][0], batch_size=b)
def make_env1(b):
return gym_tensorflow.make(game=exp["games"][1], batch_size=b)
# if f_isSingleTask(exp):
# workers = [ ConcurrentWorkers(make_env0, Model, batch_size=64) ]
# else:
# workers = [
# ConcurrentWorkers(make_env0, Model, batch_size=32),
# ConcurrentWorkers(make_env1, Model, batch_size=32)
# ]
workers = [
ConcurrentWorkers(make_env0,
Model,
batch_size=32 * (1 + f_isSingleTask(exp))),
ConcurrentWorkers(make_env1,
Model,
batch_size=32 * (1 - f_isSingleTask(exp)))
]
tlogger.info('Start timing')
tstart = time.time()
tf_sess = tf.Session()
tf_sess.run(tf.global_variables_initializer())
state = TrainingState(exp)
state.initialize(rs, noise, workers[0].model)
workers[0].initialize(tf_sess)
workers[1].initialize(tf_sess)
if not f_isSingleTask(exp):
raise RuntimeError(
"This script is only designed to run single task. Check your configuration and make sure both games have the same name"
)
for iteration in range(exp['iterations']):
tlogger.info("BEGINNING ITERATION: {}".format(iteration))
# override state using one from a previous run
state = load_pickle(iteration, exp['model_logdir'], "state")
##############
### GAME 0 ###
##############
worker = workers[0]
frames_computed_so_far = tf_sess.run(worker.steps_counter)
game0_results = []
game0_rewards = []
game0_episode_lengths = []
iterator = iter(
worker.monitor_eval(make_offspring(exp, noise, rs, worker, state),
max_frames=state.tslimit * 4))
for pos_seeds, pos_reward, pos_length in iterator:
neg_seeds, neg_reward, neg_length = next(iterator)
assert pos_seeds == neg_seeds
result = Offspring(pos_seeds, [pos_reward, neg_reward],
[pos_length, neg_length])
rewards = result.rewards
game0_results.append(result)
game0_rewards.append(rewards)
game0_episode_lengths.append(result.ep_len)
state.num_frames += tf_sess.run(
worker.steps_counter) - frames_computed_so_far
game0_returns_n2 = np.array([a.rewards for a in game0_results])
game0_noise_inds_n = [a.seeds for a in game0_results]
tlogger.info("game0 rewards: {}".format(np.mean(game0_rewards)))
tlogger.info("game0 eplens: {}".format(np.mean(game0_episode_lengths)))
save_pickle(iteration, log_dir, "game0_rewards", game0_rewards)
save_pickle(iteration, log_dir, "game0_episode_lengths",
game0_episode_lengths)
tlogger.info("Saving offsprings seeds")
save_pickle(iteration, log_dir, "offsprings_seeds", game0_noise_inds_n)
save_pickle(iteration, log_dir, "state", state)
######################
### EVALUATE ELITE ###
######################
_, test_evals, test_timesteps = workers[0].monitor_eval_repeated(
[(state.theta, 0)],
max_frames=None,
num_episodes=exp['num_test_episodes'] //
(2**(1 - f_isSingleTask(exp))))[0]
tlogger.info("game0 elite: {}".format(np.mean(test_evals)))
save_pickle(iteration, log_dir, 'game0_elite', test_evals)
save_pickle(iteration, log_dir, 'game0_elite_timestemps',
test_timesteps)
state.num_frames += tf_sess.run(
worker.steps_counter) - frames_computed_so_far
state.it += 1
os.kill(os.getpid(), signal.SIGTERM)
