class CERL_Agent:
"""Main CERL class containing all methods for CERL
Parameters:
args (int): Parameter class with all the parameters
"""
def __init__(self, args):
self.args = args
self.evolver = SSNE(self.args)
#MP TOOLS
self.manager = Manager()
#Genealogy tool
self.genealogy = Genealogy()
#Initialize population
self.pop = self.manager.list()
for _ in range(args.pop_size):
wwid = self.genealogy.new_id('evo')
if ALGO == 'SAC':
self.pop.append(
GaussianPolicy(args.state_dim, args.action_dim,
args.hidden_size, wwid))
else:
self.pop.append(Actor(args.state_dim, args.action_dim, wwid))
if ALGO == "SAC":
self.best_policy = GaussianPolicy(args.state_dim, args.action_dim,
args.hidden_size, -1)
else:
self.best_policy = Actor(args.state_dim, args.action_dim, -1)
#Turn off gradients and put in eval mod
for actor in self.pop:
actor = actor.cpu()
actor.eval()
#Init BUFFER
self.replay_buffer = Buffer(1000000, self.args.buffer_gpu)
#Intialize portfolio of learners
self.portfolio = []
self.portfolio = initialize_portfolio(self.portfolio, self.args,
self.genealogy, PORTFOLIO_ID)
self.rollout_bucket = self.manager.list()
for _ in range(len(self.portfolio)):
if ALGO == 'SAC':
self.rollout_bucket.append(
GaussianPolicy(args.state_dim, args.action_dim,
args.hidden_size, -1))
else:
self.rollout_bucket.append(
Actor(args.state_dim, args.action_dim, -1))
# Initialize shared data bucket
self.data_bucket = self.replay_buffer.tuples
############## MULTIPROCESSING TOOLS ###################
#Evolutionary population Rollout workers
self.evo_task_pipes = [Pipe() for _ in range(args.pop_size)]
self.evo_result_pipes = [Pipe() for _ in range(args.pop_size)]
self.evo_workers = [
Process(target=rollout_worker,
args=(id, self.evo_task_pipes[id][1],
self.evo_result_pipes[id][0], False,
self.data_bucket, self.pop, ENV_NAME, None, ALGO))
for id in range(args.pop_size)
]
for worker in self.evo_workers:
worker.start()
self.evo_flag = [True for _ in range(args.pop_size)]
#Learner rollout workers
self.task_pipes = [Pipe() for _ in range(args.rollout_size)]
self.result_pipes = [Pipe() for _ in range(args.rollout_size)]
self.workers = [
Process(target=rollout_worker,
args=(id, self.task_pipes[id][1], self.result_pipes[id][0],
True, self.data_bucket, self.rollout_bucket,
ENV_NAME, args.noise_std, ALGO))
for id in range(args.rollout_size)
]
for worker in self.workers:
worker.start()
self.roll_flag = [True for _ in range(args.rollout_size)]
#Test bucket
self.test_bucket = self.manager.list()
if ALGO == 'SAC':
self.test_bucket.append(
GaussianPolicy(args.state_dim, args.action_dim,
args.hidden_size, -1))
else:
self.test_bucket.append(Actor(args.state_dim, args.action_dim, -1))
#5 Test workers
self.test_task_pipes = [Pipe() for _ in range(TEST_SIZE)]
self.test_result_pipes = [Pipe() for _ in range(TEST_SIZE)]
self.test_workers = [
Process(target=rollout_worker,
args=(id, self.test_task_pipes[id][1],
self.test_result_pipes[id][0], False, None,
self.test_bucket, ENV_NAME, args.noise_std, ALGO))
for id in range(TEST_SIZE)
]
for worker in self.test_workers:
worker.start()
self.test_flag = False
#Meta-learning controller (Resource Distribution)
self.allocation = [
] #Allocation controls the resource allocation across learners
for i in range(args.rollout_size):
self.allocation.append(
i % len(self.portfolio)) #Start uniformly (equal resources)
#self.learner_stats = [{'fitnesses': [], 'ep_lens': [], 'value': 0.0, 'visit_count':0} for _ in range(len(self.portfolio))] #Track node statistsitic (each node is a learner), to compute UCB scores
#Trackers
self.best_score = 0.0
self.gen_frames = 0
self.total_frames = 0
self.best_shaped_score = None
self.test_score = None
self.test_std = None
def train(self, gen, frame_tracker):
"""Main training loop to do rollouts, neureoevolution, and policy gradients
Parameters:
gen (int): Current epoch of training
Returns:
None
"""
################ START ROLLOUTS ##############
#Start Evolution rollouts
if not ISOLATE_PG:
for id, actor in enumerate(self.pop):
if self.evo_flag[id]:
self.evo_task_pipes[id][0].send(id)
self.evo_flag[id] = False
#Sync all learners actor to cpu (rollout) actor
for i, learner in enumerate(self.portfolio):
learner.algo.actor.cpu()
utils.hard_update(self.rollout_bucket[i], learner.algo.actor)
learner.algo.actor.cuda()
# Start Learner rollouts
for rollout_id, learner_id in enumerate(self.allocation):
if self.roll_flag[rollout_id]:
self.task_pipes[rollout_id][0].send(learner_id)
self.roll_flag[rollout_id] = False
#Start Test rollouts
if gen % 5 == 0:
self.test_flag = True
for pipe in self.test_task_pipes:
pipe[0].send(0)
############# UPDATE PARAMS USING GRADIENT DESCENT ##########
if self.replay_buffer.__len__(
) > self.args.batch_size * 10: ###BURN IN PERIOD
self.replay_buffer.tensorify(
) # Tensorify the buffer for fast sampling
#Spin up threads for each learner
threads = [
threading.Thread(
target=learner.update_parameters,
args=(self.replay_buffer, self.args.buffer_gpu,
self.args.batch_size,
int(self.gen_frames * self.args.gradperstep)))
for learner in self.portfolio
]
# Start threads
for thread in threads:
thread.start()
#Join threads
for thread in threads:
thread.join()
self.gen_frames = 0
########## SOFT -JOIN ROLLOUTS FOR EVO POPULATION ############
if not ISOLATE_PG:
all_fitness = []
all_net_ids = []
all_eplens = []
while True:
for i in range(self.args.pop_size):
if self.evo_result_pipes[i][1].poll():
entry = self.evo_result_pipes[i][1].recv()
all_fitness.append(entry[1])
all_net_ids.append(entry[0])
all_eplens.append(entry[2])
self.gen_frames += entry[2]
self.total_frames += entry[2]
self.evo_flag[i] = True
# Soft-join (50%)
if len(all_fitness
) / self.args.pop_size >= self.args.asynch_frac:
break
########## HARD -JOIN ROLLOUTS FOR LEARNER ROLLOUTS ############
for i in range(self.args.rollout_size):
entry = self.result_pipes[i][1].recv()
learner_id = entry[0]
fitness = entry[1]
num_frames = entry[2]
self.portfolio[learner_id].update_stats(fitness, num_frames)
self.gen_frames += num_frames
self.total_frames += num_frames
if fitness > self.best_score: self.best_score = fitness
self.roll_flag[i] = True
#Referesh buffer (housekeeping tasks - pruning to keep under capacity)
self.replay_buffer.referesh()
######################### END OF PARALLEL ROLLOUTS ################
############ PROCESS MAX FITNESS #############
if not ISOLATE_PG:
champ_index = all_net_ids[all_fitness.index(max(all_fitness))]
utils.hard_update(self.test_bucket[0], self.pop[champ_index])
if max(all_fitness) > self.best_score:
self.best_score = max(all_fitness)
utils.hard_update(self.best_policy, self.pop[champ_index])
if SAVE:
torch.save(
self.pop[champ_index].state_dict(),
self.args.aux_folder + ENV_NAME + '_best' + SAVETAG)
print("Best policy saved with score",
'%.2f' % max(all_fitness))
else: #Run PG in isolation
utils.hard_update(self.test_bucket[0], self.rollout_bucket[0])
###### TEST SCORE ######
if self.test_flag:
self.test_flag = False
test_scores = []
for pipe in self.test_result_pipes: #Collect all results
entry = pipe[1].recv()
test_scores.append(entry[1])
test_scores = np.array(test_scores)
test_mean = np.mean(test_scores)
test_std = (np.std(test_scores))
# Update score to trackers
frame_tracker.update([test_mean], self.total_frames)
else:
test_mean, test_std = None, None
#NeuroEvolution's probabilistic selection and recombination step
if not ISOLATE_PG:
if gen % 5 == 0:
self.evolver.epoch(gen, self.genealogy, self.pop, all_net_ids,
all_fitness, self.rollout_bucket)
else:
self.evolver.epoch(gen, self.genealogy, self.pop, all_net_ids,
all_fitness, [])
#META LEARNING - RESET ALLOCATION USING UCB
if gen % 1 == 0:
self.allocation = ucb(len(self.allocation), self.portfolio,
self.args.ucb_coefficient)
#Metrics
if not ISOLATE_PG:
champ_len = all_eplens[all_fitness.index(max(all_fitness))]
champ_wwid = int(self.pop[champ_index].wwid.item())
max_fit = max(all_fitness)
else:
champ_len = num_frames
champ_wwid = int(self.rollout_bucket[0].wwid.item())
all_fitness = [fitness]
max_fit = fitness
all_eplens = [num_frames]
return max_fit, champ_len, all_fitness, all_eplens, test_mean, test_std, champ_wwid
class SAC_Discrete_Trainer:
"""Main CERL class containing all methods for CERL
Parameters:
args (object): Parameter class with all the parameters
"""
def __init__(self, args, model_constructor, env_constructor):
self.args = args
#MP TOOLS
self.manager = Manager()
#Algo
self.algo = SAC_Discrete(args, model_constructor, args.gamma)
# #Save best policy
# self.best_policy = model_constructor.make_model('actor')
#Init BUFFER
self.replay_buffer = Buffer(args.buffer_size)
self.data_bucket = self.replay_buffer.tuples
#Initialize Rollout Bucket
self.rollout_bucket = self.manager.list()
self.rollout_bucket.append(model_constructor.make_model('Gumbel_FF'))
############## MULTIPROCESSING TOOLS ###################
#Learner rollout workers
self.task_pipes = [Pipe() for _ in range(args.rollout_size)]
self.result_pipes = [Pipe() for _ in range(args.rollout_size)]
self.workers = [
Process(target=rollout_worker,
args=(id, 'pg', self.task_pipes[id][1],
self.result_pipes[id][0], self.data_bucket,
self.rollout_bucket, env_constructor))
for id in range(args.rollout_size)
]
for worker in self.workers:
worker.start()
self.roll_flag = [True for _ in range(args.rollout_size)]
#Test bucket
self.test_bucket = self.manager.list()
self.test_bucket.append(model_constructor.make_model('Gumbel_FF'))
#5 Test workers
self.test_task_pipes = [
Pipe() for _ in range(env_constructor.dummy_env.test_size)
]
self.test_result_pipes = [
Pipe() for _ in range(env_constructor.dummy_env.test_size)
]
self.test_workers = [
Process(target=rollout_worker,
args=(id, 'test', self.test_task_pipes[id][1],
self.test_result_pipes[id][0], None,
self.test_bucket, env_constructor))
for id in range(env_constructor.dummy_env.test_size)
]
for worker in self.test_workers:
worker.start()
self.test_flag = False
#Trackers
self.best_score = 0.0
self.gen_frames = 0
self.total_frames = 0
self.test_score = None
self.test_std = None
self.test_trace = []
self.rollout_fits_trace = []
self.ep_len = 0
self.r1_reward = 0
self.num_footsteps = 0
def forward_epoch(self, epoch, tracker):
"""Main training loop to do rollouts, neureoevolution, and policy gradients
Parameters:
gen (int): Current epoch of training
Returns:
None
"""
################ START ROLLOUTS ##############
#Sync all learners actor to cpu (rollout) actor
self.algo.actor.cpu()
utils.hard_update(self.rollout_bucket[0], self.algo.actor)
utils.hard_update(self.test_bucket[0], self.algo.actor)
self.algo.actor.cuda()
# Start Learner rollouts
for rollout_id in range(self.args.rollout_size):
if self.roll_flag[rollout_id]:
self.task_pipes[rollout_id][0].send(0)
self.roll_flag[rollout_id] = False
#Start Test rollouts
if epoch % 1 == 0:
self.test_flag = True
for pipe in self.test_task_pipes:
pipe[0].send(0)
############# UPDATE PARAMS USING GRADIENT DESCENT ##########
if self.replay_buffer.__len__(
) > self.args.learning_start: ###BURN IN PERIOD
#self.replay_buffer.tensorify() # Tensorify the buffer for fast sampling
for _ in range(self.gen_frames):
s, ns, a, r, done = self.replay_buffer.sample(
self.args.batch_size)
if torch.cuda.is_available():
s = s.cuda()
ns = ns.cuda()
a = a.cuda()
r = r.cuda()
done = done.cuda()
r = r * self.args.reward_scaling
self.algo.update_parameters(s, ns, a, r, done)
self.gen_frames = 0
########## HARD -JOIN ROLLOUTS FOR LEARNER ROLLOUTS ############
if self.args.rollout_size > 0:
for i in range(self.args.rollout_size):
entry = self.result_pipes[i][1].recv()
learner_id = entry[0]
fitness = entry[1]
num_frames = entry[2]
self.rollout_fits_trace.append(fitness)
self.gen_frames += num_frames
self.total_frames += num_frames
if fitness > self.best_score: self.best_score = fitness
self.roll_flag[i] = True
#Referesh buffer (housekeeping tasks - pruning to keep under capacity)
self.replay_buffer.referesh()
######################### END OF PARALLEL ROLLOUTS ################
###### TEST SCORE ######
if self.test_flag:
self.test_flag = False
test_scores = []
eplens = []
r1_reward = []
num_footsteps = []
for pipe in self.test_result_pipes: #Collect all results
entry = pipe[1].recv()
test_scores.append(entry[1])
eplens.append(entry[3])
r1_reward.append(entry[4])
num_footsteps.append(entry[5])
test_scores = np.array(test_scores)
test_mean = np.mean(test_scores)
test_std = (np.std(test_scores))
self.test_trace.append(test_mean)
self.num_footsteps = np.mean(np.array(num_footsteps))
self.ep_len = np.mean(np.array(eplens))
self.r1_reward = np.mean(np.array(r1_reward))
tracker.update([test_mean, self.r1_reward], self.total_frames)
if self.r1_reward > self.best_score:
self.best_score = self.r1_reward
torch.save(
self.test_bucket[0].state_dict(),
self.args.aux_folder + 'bestR1_' + self.args.savetag)
print("Best R1 Policy saved with score",
'%.2f' % self.r1_reward)
else:
test_mean, test_std = None, None
if epoch % 20 == 0:
#Save models
torch.save(self.algo.actor.state_dict(),
self.args.aux_folder + 'actor_' + self.args.savetag)
torch.save(self.algo.critic.state_dict(),
self.args.aux_folder + 'critic_' + self.args.savetag)
print("Actor and Critic saved")
return test_mean, test_std
def train(self, frame_limit):
# Define Tracker class to track scores
test_tracker = utils.Tracker(
self.args.savefolder,
['score_' + self.args.savetag, 'r1_' + self.args.savetag],
'.csv') # Tracker class to log progress
time_start = time.time()
for gen in range(1, 1000000000): # Infinite generations
# Train one iteration
test_mean, test_std = self.forward_epoch(gen, test_tracker)
print('Gen/Frames', gen, '/', self.total_frames, 'max_ever:',
'%.2f' % self.best_score, ' Avg:',
'%.2f' % test_tracker.all_tracker[0][1], ' Frames/sec:',
'%.2f' % (self.total_frames / (time.time() - time_start)),
' Test/RolloutScore',
['%.2f' % i for i in self.test_trace[-1:]],
'%.2f' % self.rollout_fits_trace[-1], 'Ep_len',
'%.2f' % self.ep_len, '#Footsteps',
'%.2f' % self.num_footsteps, 'R1_Reward',
'%.2f' % self.r1_reward, 'savetag', self.args.savetag)
if gen % 5 == 0:
print()
print('Entropy', self.algo.entropy['mean'], 'Next_Entropy',
self.algo.next_entropy['mean'], 'Temp',
self.algo.temp['mean'], 'Poilcy_Q',
self.algo.policy_q['mean'], 'Critic_Loss',
self.algo.critic_loss['mean'])
print()
if self.total_frames > frame_limit:
break
class CERL_Agent:
"""Main CERL class containing all methods for CERL
Parameters:
args (int): Parameter class with all the parameters
"""
def __init__(self,
args): # need to intialize rollout_workers to have blue agent
self.args = args
self.evolver = SSNE(
self.args) # this evolver implements neuro-evolution
# MP TOOLS
self.manager = Manager()
self.mutate_algos = [
Mutation_Add(self),
Mutation_Delete(self),
Mutation_Exchange(self)
] #store all the mutate algorithm objects
# Genealogy tool
self.genealogy = Genealogy()
# Init BUFFER
self.replay_buffer = Buffer(1000000, self.args.buffer_gpu)
#if SA_FLAG:
self.metrics = []
self.last_portfolio = None
self.T_max = 30
self.T = self.T_max
self.T_min = 0.2
self.decay_rate = 0.975
# Initialize population
self.pop = self.manager.list()
for _ in range(args.pop_size):
wwid = self.genealogy.new_id('evo')
if ALGO == 'SAC':
self.pop.append(
GaussianPolicy(args.state_dim, args.action_dim,
args.hidden_size, wwid))
elif ALGO == 'TD3':
self.pop.append(
Actor(args.state_dim, args.action_dim, wwid, ALGO))
# use ALGO to distinguish differe net architecture
elif ALGO == 'dis' or 'TD3_tennis':
self.pop.append(
Actor(args.state_dim, args.action_dim, wwid, ALGO))
else:
assert False, "invalid algorithm type"
if ALGO == "SAC":
self.best_policy = GaussianPolicy(args.state_dim, args.action_dim,
args.hidden_size, -1)
else:
self.best_policy = Actor(args.state_dim, args.action_dim, -1, ALGO)
if ALGO == 'dis':
self.average_policy = AverageActor(args.state_dim,
args.action_dim,
-2,
ALGO,
self.pop,
self.replay_buffer,
args.buffer_gpu,
args.batch_size,
iterations=10)
self.average_policy.share_memory()
self.best_policy.share_memory()
# added by macheng, share the best policy accross processes (used as internal belief update models for blue)
# now we assign shared blue_trainer, we should train this agent such that the roll_out workers are also up to date
# should make sure that self.best_policy (emergent learner) is also shared
if ALGO == 'dis' or 'TD3_tennis':
assert hasattr(
args, "blue_trainer"
), "must have blue_agent trainer to intialize rollout_worker, see line 109, class Parameter definition"
if ALGO == 'dis':
trainers = [args.blue_trainer, self.average_policy]
else:
trainers = [args.blue_trainer, None
] if ALGO == 'TD3_tennis' else []
self.trainers = trainers
self.blue_dqn = args.blue_trainer
# Turn off gradients and put in eval mod
for actor in self.pop:
actor = actor.cpu()
actor.eval()
# Intialize portfolio of learners
self.portfolio = []
self.portfolio = initialize_portfolio(self.portfolio, self.args,
self.genealogy, PORTFOLIO_ID)
self.complement_portfolio = [
] #complementary of the portfolio, whatever not in the portfolio should be stored here
self.total_rollout_bucket = self.manager.list(
) #macheng: we use total_rollout_bucket to represents the whole set of rollout models, now rollout_bukcet dynamically resize according to portforlio, for SA
self.rollout_bucket = self.total_rollout_bucket
#self.rollout_bucket = self.manager.list()
#print("rollout_bucker needs to be updated, main.py line 239 ")
for _ in range(len(self.portfolio)):
if ALGO == 'SAC':
self.rollout_bucket.append(
GaussianPolicy(args.state_dim, args.action_dim,
args.hidden_size, -1))
else:
self.rollout_bucket.append(
Actor(args.state_dim, args.action_dim, -1, ALGO))
# Initialize shared data bucket
self.data_bucket = self.replay_buffer.tuples
############## MULTIPROCESSING TOOLS ###################
# Evolutionary population Rollout workers
self.evo_task_pipes = [Pipe() for _ in range(args.pop_size)]
self.evo_result_pipes = [Pipe() for _ in range(args.pop_size)]
self.evo_workers = [
Process(target=rollout_worker,
args=(id, 0, self.evo_task_pipes[id][1],
self.evo_result_pipes[id][0], False,
self.data_bucket, self.pop, ENV_NAME, None, ALGO,
self.trainers)) for id in range(args.pop_size)
]
for worker in self.evo_workers:
worker.start()
self.evo_flag = [True for _ in range(args.pop_size)]
# Learner rollout workers
self.task_pipes = [Pipe() for _ in range(args.rollout_size)]
self.result_pipes = [Pipe() for _ in range(args.rollout_size)]
self.workers = [
Process(target=rollout_worker,
args=(id, 1, self.task_pipes[id][1],
self.result_pipes[id][0], True, self.data_bucket,
self.rollout_bucket, ENV_NAME, args.noise_std, ALGO,
self.trainers)) for id in range(args.rollout_size)
]
for worker in self.workers:
worker.start()
self.roll_flag = [True for _ in range(args.rollout_size)]
# Test bucket
self.test_bucket = self.manager.list()
if ALGO == 'SAC':
self.test_bucket.append(
GaussianPolicy(args.state_dim, args.action_dim,
args.hidden_size, -1))
else:
self.test_bucket.append(
Actor(args.state_dim, args.action_dim, -1, ALGO))
# 5 Test workers
self.test_task_pipes = [Pipe() for _ in range(TEST_SIZE)]
self.test_result_pipes = [Pipe() for _ in range(TEST_SIZE)]
self.test_workers = [
Process(target=rollout_worker,
args=(id, 2, self.test_task_pipes[id][1],
self.test_result_pipes[id][0], False, None,
self.test_bucket, ENV_NAME, args.noise_std, ALGO,
self.trainers)) for id in range(TEST_SIZE)
]
for worker in self.test_workers:
worker.start()
self.test_flag = False
# Meta-learning controller (Resource Distribution)
self.allocation = [
] #Allocation controls the resource allocation across learners
for i in range(args.rollout_size):
self.allocation.append(
i % len(self.portfolio)) #Start uniformly (equal resources)
# self.learner_stats = [{'fitnesses': [], 'ep_lens': [], 'value': 0.0, 'visit_count':0} for _ in range(len(self.portfolio))] #Track node statistsitic (each node is a learner), to compute UCB scores
# Trackers
self.best_score = -np.inf
self.gen_frames = 0
self.total_frames = 0
self.best_shaped_score = None
self.test_score = None
self.test_std = None
# trainer contains the blue_dqn to be trained, and the red model used for belief update, red_actor is the actual red agent trained against
# id is the actual red agent id
def _update_SA_temperature(self):
self.T = max(self.T * self.decay_rate, self.T_min)
def _get_accept_rate(self):
if RANDOM_WALK:
return 1.0
else:
if self.metrics[-1] > self.metrics[-2]:
return 1.0
else:
return np.exp((self.metrics[-1] - self.metrics[-2]) / self.T)
def _mutate(self):
while True:
mutate_algo_index = random.choice(range(3))
if self._try_mutate(mutate_algo_index):
return
def _try_mutate(self,
algo_index): # 0 for add, 1 for delete, 2 for exchange
return self.mutate_algos[algo_index].try_mutate()
def simulated_annealing(self, metric): #take in the current metric
self.metrics.append(metric)
if self.last_portfolio: #has last_portfolio
accept_rate = self._get_accept_rate() #based on self.metrics[-2:]
self._update_SA_temperature()
if np.random.random() > accept_rate: #reject
self.portfolio = self.last_portfolio
self.complement_portfolio = self.last_complement_portfolio
self.last_portfolio = copy.copy(
self.portfolio) #maintain a shallow copy as
self.last_complement_portfolio = copy.copy(self.complement_portfolio)
self._mutate() #perturb the portfolio
# update rollout_bucket size, only the first len(self.portfolio) rollout_buckets are visible
self.update_rollout_bucket()
# update allocation, to be compatible with the current portfolio
self.update_allocation()
def update_rollout_bucket(self):
self.rollout_bucket = self.total_rollout_bucket[:len(self.portfolio)]
def train_blue_dqn(
self,
trainers,
env_name,
gen,
ALGO='dis',
pomdp_adv=False
): #in this method, rollout and training are done together, opponent sampled from the population
NUM_EPISODE = 100 #train 100 episodes for the blue to converge to the new best response to red
EPS_START = max(1.0 * 0.5**(gen - 10),
0.15) if gen >= 10 else 1.0 #initial epsilon
EPS_END = 0.05
EPS_DECAY = 0.995
if ALGO == 'dis': # make env with blue and red policy agent inside,
assert trainers is not None
dis_env = make_self_play_env(
seed=np.random.choice(np.array(range(len(self.pop)))),
return_policy_agent=False,
trainers=trainers
)[0] # trainer if not None, first is the shared DQN agent, second is the best red policy
env = EnvironmentWrapper(
env_name, ALGO, dis_env,
0) # the "0" is the index for training blue agent
elif ALGO == 'TD3_tennis':
no_graphics = not RENDER
tennis_env = make_tennis_env.TennisEnvFactory(
seed=np.random.choice(np.array(range(len(self.pop)))),
no_graphics=no_graphics,
pid=-1).getEnv()[0]
env = EnvironmentWrapper('Tennis', ALGO, tennis_env, 0)
else:
env = EnvironmentWrapper(env_name, ALGO)
blue_dqn = trainers[0]
average_reward = 0
eps = EPS_START
average_red_reward = 0
red_count = 0
average_actual_blue_reward = 0
blue_count = 0
for it in range(NUM_EPISODE):
if not pomdp_adv: #if pomdp_adv, make sure that TD3_actor is never used
id = np.random.choice(np.array(range(len(self.pop))))
red_actor = self.pop[id]
env.set_TD3_actor(red_actor)
fitness = 0.0
#here fitness if simplely reward
total_frame = 0
state = env.reset()
env.randomize_neu_adv()
if pomdp_adv:
env.try_set_pomdp_adv(
) #try to set if opponent to pomdp adv if opponent is adversary, else do nothing
render_flag = (np.random.random() < 0.05)
while True: # unless done
action = blue_dqn.act(state, eps=eps)
# action = utils.to_numpy(action)
next_state, reward, done, info = env.step(
copy.deepcopy(action), use_actual_reward=DRQN
) #after calling env.step, evaluator initialized later does not work
#should be something wrong with the internal red model?
blue_dqn.step(state, action, reward, next_state, done)
if render_flag and self.args.render:
env.render()
# next_state = utils.to_tensor(np.array(next_state)).unsqueeze(0)
state = next_state
fitness += reward
total_frame += 1
# DONE FLAG IS Received
if done:
average_red_reward += env.get_red_reward(
) if env.get_red_reward() is not None else 0
average_actual_blue_reward += env.get_blue_actual_reward(
) if env.get_blue_actual_reward() is not None else 0
red_count += 1 if env.get_red_reward() is not None else 0
blue_count += 1 if env.get_blue_actual_reward(
) is not None else 0
if render_flag: env.env.close()
break
average_reward += fitness
eps = max(EPS_END, EPS_DECAY * eps)
if gen >= 10 and gen % 5 == 0:
blue_dqn.save_net('./pytorch_models/train_blue_dqn_step_' +
str(gen) + '.pth')
average_reward /= NUM_EPISODE
if red_count != 0:
average_red_reward /= red_count
if blue_count != 0:
average_actual_blue_reward /= blue_count
return average_reward, average_red_reward, average_actual_blue_reward
def evaluate_training_fixed_blue(
self): #this evaluate against the training opponent (red pop)
self.evaluator.pomdp_adv = False
return self.evaluator.evaluate_fixed_agents(self.trainers[0],
self.trainers[1], self.pop)
def train(self, gen, frame_tracker):
"""Main training loop to do rollouts, neureoevolution, and policy gradients
Parameters:
gen (int): Current epoch of training
Returns:
None
"""
################ START ROLLOUTS ##############
# Start Evolution rollouts
if not ISOLATE_PG:
for id, actor in enumerate(self.pop):
if self.evo_flag[id]:
self.evo_task_pipes[id][0].send((id, gen))
self.evo_flag[id] = False
# Sync all learners actor to cpu (rollout) actor
# (update rollout parameter using the learner parameter, such that rollout worker is up to date)
for i, learner in enumerate(self.portfolio): #number of learner
learner.algo.actor.cpu()
utils.hard_update(
self.rollout_bucket[i], learner.algo.actor
) #rollout bucket is now synchronized with learner to perform rollout for learner actors
if torch.cuda.is_available(): learner.algo.actor.cuda()
# Start Learner rollouts
for rollout_id, learner_id in enumerate(
self.allocation): #number of rollout_size
if self.roll_flag[rollout_id]:
self.task_pipes[rollout_id][0].send(
(learner_id, gen)
) #allocation record the id of the learner that bucket should run, so rollout_id is the id of rollout_bucket
self.roll_flag[rollout_id] = False
# Start Test rollouts
if gen % 5 == 0:
self.test_flag = True
for pipe in self.test_task_pipes:
pipe[0].send((0, gen))
############# UPDATE PARAMS USING GRADIENT DESCENT ##########
# main training loop
if self.replay_buffer.__len__(
) > self.args.batch_size * 10: ###BURN IN PERIOD
self.replay_buffer.tensorify(
) # Tensorify the buffer for fast sampling
# Spin up threads for each learner
threads = [
threading.Thread(
target=learner.update_parameters,
args=(self.replay_buffer, self.args.buffer_gpu,
self.args.batch_size,
int(self.gen_frames * self.args.gradperstep)))
for learner in self.portfolio
] #macheng: do we want to train all the learners?
# Start threads
for thread in threads:
thread.start()
# Join threads
for thread in threads:
thread.join()
# Now update average_policy
#self.average_policy.cuda()
if ALGO == 'dis':
self.average_policy.update(
) #update the average_policy parameter with supervised learning
self.gen_frames = 0
#########Visualize Learner Critic Function#################
# if self.replay_buffer.__len__() % 2500 == 0:
# visualize_critic(self.portfolio[2], make_self_play_env(trainers=[[],[]])[0], 50) #arguments: Learner, env, N_GRID
########## SOFT -JOIN ROLLOUTS FOR EVO POPULATION ############
if not ISOLATE_PG:
all_fitness = []
all_net_ids = []
all_eplens = []
while True:
for i in range(self.args.pop_size):
if self.evo_result_pipes[i][1].poll():
entry = self.evo_result_pipes[i][1].recv()
all_fitness.append(entry[1])
all_net_ids.append(entry[0])
all_eplens.append(entry[2])
self.gen_frames += entry[2]
self.total_frames += entry[2]
self.evo_flag[i] = True
# Soft-join (50%)
if len(all_fitness
) / self.args.pop_size >= self.args.asynch_frac:
break
########## HARD -JOIN ROLLOUTS FOR LEARNER ROLLOUTS ############
for i in range(self.args.rollout_size):
entry = self.result_pipes[i][1].recv()
learner_id = entry[0]
fitness = entry[1]
num_frames = entry[2]
self.portfolio[learner_id].update_stats(fitness, num_frames)
self.gen_frames += num_frames
self.total_frames += num_frames
if fitness > self.best_score: self.best_score = fitness
self.roll_flag[i] = True
# Referesh buffer (housekeeping tasks - pruning to keep under capacity)
self.replay_buffer.referesh()
######################### END OF PARALLEL ROLLOUTS ################
############ PROCESS MAX FITNESS #############
# ms:best policy is always up to date
# so here the best learner is saved
if not ISOLATE_PG:
champ_index = all_net_ids[all_fitness.index(max(all_fitness))]
utils.hard_update(self.test_bucket[0], self.pop[champ_index])
if max(all_fitness) > self.best_score:
self.best_score = max(all_fitness)
utils.hard_update(self.best_policy, self.pop[champ_index])
if SAVE:
torch.save(
self.pop[champ_index].state_dict(),
self.args.aux_folder + ENV_NAME + '_best' + SAVETAG)
print("Best policy saved with score",
'%.2f' % max(all_fitness))
else: #Run PG in isolation
utils.hard_update(self.test_bucket[0], self.rollout_bucket[0])
###### TEST SCORE ######
if self.test_flag:
self.test_flag = False
test_scores = []
for pipe in self.test_result_pipes: #Collect all results
entry = pipe[1].recv()
test_scores.append(entry[1])
test_scores = np.array(test_scores)
test_mean = np.mean(test_scores)
test_std = (np.std(test_scores))
# Update score to trackers
frame_tracker.update([test_mean], self.total_frames)
else:
test_mean, test_std = None, None
# NeuroEvolution's probabilistic selection and recombination step
# ms: this epoch() method implements neuro-evolution
if not ISOLATE_PG: #seems pop_size and rollout_size must be 10, otherwise this will produce error
if gen % 5 == 0:
self.evolver.epoch(
gen, self.genealogy, self.pop, all_net_ids, all_fitness,
self.rollout_bucket
) #this method also copies learner to evoler
else:
self.evolver.epoch(gen, self.genealogy, self.pop, all_net_ids,
all_fitness, [])
# META LEARNING - RESET ALLOCATION USING UCB
if gen % 1 == 0:
self.update_allocation()
# Metrics
if not ISOLATE_PG:
champ_len = all_eplens[all_fitness.index(max(all_fitness))]
champ_wwid = int(self.pop[champ_index].wwid.item())
max_fit = max(all_fitness)
else:
champ_len = num_frames
champ_wwid = int(self.rollout_bucket[0].wwid.item())
all_fitness = [fitness]
max_fit = fitness
all_eplens = [num_frames]
return max_fit, champ_len, all_fitness, all_eplens, test_mean, test_std, champ_wwid
def update_allocation(self):
self.allocation = ucb(len(self.allocation), self.portfolio,
self.args.ucb_coefficient)
def sim_and_eval_POMDP(self):
self.evaluator = Evaluator(
self, 5, self.trainers,
pomdp_adv=True) # evaluator must be created before train_dqn
for gen in range(1000000):
print('gen=', gen)
blue_score, red_score, actual_blue_score = agent.train_blue_dqn(
agent.trainers, ENV_NAME, gen, ALGO='dis', pomdp_adv=True)
print('Env', ENV_NAME, 'Gen', gen,
", Training average: Blue agent score: ", blue_score,
" Red score: ", red_score, " Actual blue score: ",
actual_blue_score)
blue_score, red_score, actual_blue_score = self.evaluator.evaluate(
)
print("Evaluation result: Blue agent score: ", blue_score,
" Red score: ", red_score, " Actual blue score: ",
actual_blue_score)
class Agent:
"""Learner object encapsulating a local learner
Parameters:
algo_name (str): Algorithm Identifier
state_dim (int): State size
action_dim (int): Action size
actor_lr (float): Actor learning rate
critic_lr (float): Critic learning rate
gamma (float): DIscount rate
tau (float): Target network sync generate
init_w (bool): Use kaimling normal to initialize?
**td3args (**kwargs): arguments for TD3 algo
"""
def __init__(self, args, id):
self.args = args
self.id = id
###Initalize neuroevolution module###
self.evolver = SSNE(self.args)
########Initialize population
self.manager = Manager()
self.popn = self.manager.list()
for _ in range(args.popn_size):
if args.ps == 'trunk':
self.popn.append(
MultiHeadActor(args.state_dim, args.action_dim,
args.hidden_size, args.config.num_agents))
else:
if args.algo_name == 'TD3':
self.popn.append(
Actor(args.state_dim,
args.action_dim,
args.hidden_size,
policy_type='DeterministicPolicy'))
else:
self.popn.append(
Actor(args.state_dim,
args.action_dim,
args.hidden_size,
policy_type='GaussianPolicy'))
self.popn[-1].eval()
#### INITIALIZE PG ALGO #####
if args.ps == 'trunk':
if self.args.is_matd3 or args.is_maddpg:
algo_name = 'TD3' if self.args.is_matd3 else 'DDPG'
self.algo = MATD3(id, algo_name, args.state_dim,
args.action_dim, args.hidden_size,
args.actor_lr, args.critic_lr, args.gamma,
args.tau, args.savetag, args.aux_save,
args.actualize, args.use_gpu,
args.config.num_agents, args.init_w)
else:
self.algo = MultiTD3(id, args.algo_name, args.state_dim,
args.action_dim, args.hidden_size,
args.actor_lr, args.critic_lr, args.gamma,
args.tau, args.savetag, args.aux_save,
args.actualize, args.use_gpu,
args.config.num_agents, args.init_w)
else:
if args.algo_name == 'TD3':
self.algo = TD3(id, args.algo_name, args.state_dim,
args.action_dim, args.hidden_size,
args.actor_lr, args.critic_lr, args.gamma,
args.tau, args.savetag, args.aux_save,
args.actualize, args.use_gpu, args.init_w)
else:
self.algo = SAC(id, args.state_dim, args.action_dim,
args.hidden_size, args.gamma, args.critic_lr,
args.actor_lr, args.tau, args.alpha,
args.target_update_interval, args.savetag,
args.aux_save, args.actualize, args.use_gpu)
#### Rollout Actor is a template used for MP #####
self.rollout_actor = self.manager.list()
if args.ps == 'trunk':
self.rollout_actor.append(
MultiHeadActor(args.state_dim, args.action_dim,
args.hidden_size, args.config.num_agents))
else:
if args.algo_name == 'TD3':
self.rollout_actor.append(
Actor(args.state_dim,
args.action_dim,
args.hidden_size,
policy_type='DeterministicPolicy'))
else:
self.rollout_actor.append(
Actor(args.state_dim,
args.action_dim,
args.hidden_size,
policy_type='GaussianPolicy'))
#Initalize buffer
if args.ps == 'trunk':
self.buffer = [
Buffer(args.buffer_size,
buffer_gpu=False,
filter_c=args.filter_c)
for _ in range(args.config.num_agents)
]
else:
self.buffer = Buffer(args.buffer_size,
buffer_gpu=False,
filter_c=args.filter_c)
#Agent metrics
self.fitnesses = [[] for _ in range(args.popn_size)]
###Best Policy HOF####
self.champ_ind = 0
def update_parameters(self):
td3args = {
'policy_noise': 0.2,
'policy_noise_clip': 0.5,
'policy_ups_freq': 2,
'action_low': -1.0,
'action_high': 1.0
}
if self.args.ps == 'trunk':
for agent_id, buffer in enumerate(self.buffer):
if self.args.is_matd3 or self.args.is_maddpg:
buffer = self.buffer[0] #Hardcoded Hack for MADDPG
buffer.referesh()
if buffer.__len__() < 10 * self.args.batch_size:
buffer.pg_frames = 0
return ###BURN_IN_PERIOD
buffer.tensorify()
for _ in range(int(self.args.gradperstep * buffer.pg_frames)):
s, ns, a, r, done, global_reward = buffer.sample(
self.args.batch_size,
pr_rew=self.args.priority_rate,
pr_global=self.args.priority_rate)
r *= self.args.reward_scaling
if self.args.use_gpu:
s = s.cuda()
ns = ns.cuda()
a = a.cuda()
r = r.cuda()
done = done.cuda()
global_reward = global_reward.cuda()
self.algo.update_parameters(s, ns, a, r, done,
global_reward, agent_id, 1,
**td3args)
buffer.pg_frames = 0
else:
self.buffer.referesh()
if self.buffer.__len__() < 10 * self.args.batch_size:
return ###BURN_IN_PERIOD
self.buffer.tensorify()
for _ in range(int(self.args.gradperstep * self.buffer.pg_frames)):
s, ns, a, r, done, global_reward = self.buffer.sample(
self.args.batch_size,
pr_rew=self.args.priority_rate,
pr_global=self.args.priority_rate)
r *= self.args.reward_scaling
if self.args.use_gpu:
s = s.cuda()
ns = ns.cuda()
a = a.cuda()
r = r.cuda()
done = done.cuda()
global_reward = global_reward.cuda()
self.algo.update_parameters(s, ns, a, r, done, global_reward,
1, **td3args)
self.buffer.pg_frames = 0 #Reset new frame counter to 0
def evolve(self):
## One gen of evolution ###
if self.args.popn_size > 1: #If not no-evo
if self.args.scheme == 'multipoint':
#Make sure that the buffer has been refereshed and tensorified
buffer_pointer = self.buffer[
0] if self.args.ps == 'trunk' else self.buffer
if buffer_pointer.__len__() < 1000: buffer_pointer.tensorify()
if random.random() < 0.01: buffer_pointer.tensorify()
#Get sample of states from the buffer
if buffer_pointer.__len__() < 1000:
sample_size = buffer_pointer.__len__()
else:
sample_size = 1000
if sample_size == 1000 and len(buffer_pointer.sT) < 1000:
buffer_pointer.tensorify()
states, _, _, _, _, _ = buffer_pointer.sample(sample_size,
pr_rew=0.0,
pr_global=0.0)
states = states.cpu()
elif self.args.scheme == 'standard':
states = None
else:
sys.exit('Unknown Evo Scheme')
#Net indices of nets that got evaluated this generation (meant for asynchronous evolution workloads)
net_inds = [i for i in range(len(self.popn))
] #Hack for a synchronous run
#Evolve
if self.args.rollout_size > 0:
self.champ_ind = self.evolver.evolve(self.popn, net_inds,
self.fitnesses,
[self.rollout_actor[0]],
states)
else:
self.champ_ind = self.evolver.evolve(self.popn, net_inds,
self.fitnesses, [],
states)
#Reset fitness metrics
self.fitnesses = [[] for _ in range(self.args.popn_size)]
def update_rollout_actor(self):
for actor in self.rollout_actor:
self.algo.policy.cpu()
mod.hard_update(actor, self.algo.policy)
if self.args.use_gpu: self.algo.policy.cuda()
class EGRL_Trainer:
"""Main CERL class containing all methods for CERL
Parameters:
args (object): Parameter class with all the parameters
"""
def __init__(self, args, model_constructor, env_constructor,
observation_space, action_space, env, state_template,
test_envs, platform):
self.args = args
model_constructor.state_dim += 2
self.platform = platform
self.policy_string = self.compute_policy_type()
self.device = torch.device("cuda" if torch.cuda.is_available(
) else "cpu") if self.args.gpu else torch.device('cpu')
#Evolution
dram_action = torch.ones((len(state_template.x), 2)) + 1
state_template.x = torch.cat([state_template.x, dram_action], axis=1)
self.evolver = MixedSSNE(
self.args, state_template
) #GA(self.args) if args.boltzman else SSNE(self.args)
self.env_constructor = env_constructor
self.test_tracker = utils.Tracker(
self.args.plot_folder,
['score_' + self.args.savetag, 'speedup_' + self.args.savetag],
'.csv') # Tracker class to log progress
self.time_tracker = utils.Tracker(self.args.plot_folder, [
'timed_score_' + self.args.savetag,
'timed_speedup_' + self.args.savetag
], '.csv')
self.champ_tracker = utils.Tracker(self.args.plot_folder, [
'champ_score_' + self.args.savetag,
'champ_speedup_' + self.args.savetag
], '.csv')
self.pg_tracker = utils.Tracker(self.args.plot_folder, [
'pg_noisy_speedup_' + self.args.savetag,
'pg_clean_speedup_' + self.args.savetag
], '.csv')
self.migration_tracker = utils.Tracker(self.args.plot_folder, [
'selection_rate_' + self.args.savetag,
'elite_rate_' + self.args.savetag
], '.csv')
#Generalization Trackers
self.r50_tracker = utils.Tracker(self.args.plot_folder, [
'r50_score_' + self.args.savetag,
'r50_speedup_' + self.args.savetag
], '.csv')
self.r101_tracker = utils.Tracker(self.args.plot_folder, [
'r101_score_' + self.args.savetag,
'r101_speedup_' + self.args.savetag
], '.csv')
self.bert_tracker = utils.Tracker(self.args.plot_folder, [
'bert_score_' + self.args.savetag,
'bert_speedup_' + self.args.savetag
], '.csv')
self.r50_frames_tracker = utils.Tracker(self.args.plot_folder, [
'r50_score_' + self.args.savetag,
'r50_speedup_' + self.args.savetag
], '.csv')
self.r101_frames_tracker = utils.Tracker(self.args.plot_folder, [
'r101_score_' + self.args.savetag,
'r101_speedup_' + self.args.savetag
], '.csv')
self.bert_frames_tracker = utils.Tracker(self.args.plot_folder, [
'bert_score_' + self.args.savetag,
'bert_speedup_' + self.args.savetag
], '.csv')
#Genealogy tool
self.genealogy = Genealogy()
self.env = env
self.test_envs = test_envs
if self.args.use_mp:
#MP TOOLS
self.manager = Manager()
#Initialize Mixed Population
self.population = self.manager.list()
else:
self.population = []
boltzman_count = int(args.pop_size * args.ratio)
rest = args.pop_size - boltzman_count
for _ in range(boltzman_count):
self.population.append(
BoltzmannChromosome(model_constructor.num_nodes,
model_constructor.action_dim))
for _ in range(rest):
self.population.append(
model_constructor.make_model(self.policy_string))
self.population[-1].eval()
#Save best policy
self.best_policy = model_constructor.make_model(self.policy_string)
#Init BUFFER
self.replay_buffer = Buffer(args.buffer_size, state_template,
action_space,
args.aux_folder + args.savetag)
self.data_bucket = self.replay_buffer.tuples
#Intialize portfolio of learners
self.portfolio = []
if args.rollout_size > 0:
self.portfolio = initialize_portfolio(self.portfolio, self.args,
self.genealogy,
args.portfolio_id,
model_constructor)
#Initialize Rollout Bucket
self.rollout_bucket = self.manager.list() if self.args.use_mp else []
for _ in range(len(self.portfolio)):
self.rollout_bucket.append(
model_constructor.make_model(self.policy_string))
if self.args.use_mp:
############## MULTIPROCESSING TOOLS ###################
#Evolutionary population Rollout workers
data_bucket = self.data_bucket if args.rollout_size > 0 else None #If Strictly Evo - don;t store data
self.evo_task_pipes = [Pipe() for _ in range(args.pop_size)]
self.evo_result_pipes = [Pipe() for _ in range(args.pop_size)]
self.evo_workers = [
Process(target=rollout_worker,
args=(id, 'evo', self.evo_task_pipes[id][1],
self.evo_result_pipes[id][0], data_bucket,
self.population, env_constructor))
for id in range(args.pop_size)
]
for worker in self.evo_workers:
worker.start()
#Learner rollout workers
self.task_pipes = [Pipe() for _ in range(args.rollout_size)]
self.result_pipes = [Pipe() for _ in range(args.rollout_size)]
self.workers = [
Process(target=rollout_worker,
args=(id, 'pg', self.task_pipes[id][1],
self.result_pipes[id][0], data_bucket,
self.rollout_bucket, env_constructor))
for id in range(args.rollout_size)
]
for worker in self.workers:
worker.start()
self.roll_flag = [True for _ in range(args.rollout_size)]
self.evo_flag = [True for _ in range(args.pop_size)]
#Meta-learning controller (Resource Distribution)
self.allocation = [
] #Allocation controls the resource allocation across learners
for i in range(args.rollout_size):
self.allocation.append(
i % len(self.portfolio)) #Start uniformly (equal resources)
#Trackers
self.best_score = -float('inf')
self.gen_frames = 0
self.total_frames = 0
self.best_speedup = -float('inf')
self.champ_type = None
def checkpoint(self):
utils.pickle_obj(self.args.ckpt_folder + 'test_tracker',
self.test_tracker)
utils.pickle_obj(self.args.ckpt_folder + 'time_tracker',
self.time_tracker)
utils.pickle_obj(self.args.ckpt_folder + 'champ_tracker',
self.champ_tracker)
for i in range(len(self.population)):
net = self.population[i]
if net.model_type == 'BoltzmanChromosome':
utils.pickle_obj(self.args.ckpt_folder + 'Boltzman/' + str(i),
net)
else:
torch.save(net.state_dict(),
self.args.ckpt_folder + 'Gumbel/' + str(i))
self.population[i] = net
def load_checkpoint(self):
#Try to load trackers
try:
self.test_tracker = utils.unpickle_obj(self.args.ckpt_folder +
'test_tracker')
self.time_tracker = utils.unpickle_obj(self.args.ckpt_folder +
'time_tracker')
self.champ_tracker = utils.unpickle_obj(self.args.ckpt_folder +
'champ_tracker')
except:
None
gumbel_template = False
for i in range(len(self.population)):
if self.population[i].model_type == 'GumbelPolicy':
gumbel_template = self.population[i]
break
boltzman_nets = os.listdir(self.args.ckpt_folder + 'Boltzman/')
gumbel_nets = os.listdir(self.args.ckpt_folder + 'Gumbel/')
print('Boltzman seeds', boltzman_nets, 'Gumbel seeds', gumbel_nets)
gumbel_models = []
boltzman_models = []
for fname in boltzman_nets:
try:
net = utils.unpickle_obj(self.args.ckpt_folder + 'Boltzman/' +
fname)
boltzman_models.append(net)
except:
print('Failed to load',
self.args.ckpt_folder + 'Boltzman/' + fname)
for fname in gumbel_nets:
try:
model_template = copy.deepcopy(gumbel_template)
model_template.load_state_dict(
torch.load(self.args.ckpt_folder + 'Gumbel/' + fname))
model_template.eval()
gumbel_models.append(model_template)
except:
print('Failed to load',
self.args.ckpt_folder + 'Gumbel/' + fname)
for i in range(len(self.population)):
net = self.population[i]
if net.model_type == 'GumbelPolicy' and len(gumbel_models) >= 1:
seed_model = gumbel_models.pop()
utils.hard_update(net, seed_model)
elif net.model_type == 'BoltzmanChromosome' and len(
boltzman_models) >= 1:
seed_model = boltzman_models.pop()
net = seed_model
self.population[i] = net
print()
print()
print()
print()
print('Checkpoint Loading Phase Completed')
print()
print()
print()
print()
def forward_generation(self, gen, time_start):
################ START ROLLOUTS ##############
#Start Evolution rollouts
if self.args.pop_size >= 1 and self.args.use_mp:
for id, actor in enumerate(self.population):
if self.evo_flag[id]:
self.evo_task_pipes[id][0].send(id)
self.evo_flag[id] = False
#If Policy Gradient
if self.args.rollout_size > 0:
#Sync all learners actor to cpu (rollout) actor
for i, learner in enumerate(self.portfolio):
learner.algo.actor.cpu()
utils.hard_update(self.rollout_bucket[i], learner.algo.actor)
learner.algo.actor.to(self.device)
# Start Learner rollouts
if self.args.use_mp:
for rollout_id, learner_id in enumerate(self.allocation):
if self.roll_flag[rollout_id]:
self.task_pipes[rollout_id][0].send(learner_id)
self.roll_flag[rollout_id] = False
############# UPDATE PARAMS USING GRADIENT DESCENT ##########
if self.replay_buffer.__len__(
) > self.args.learning_start and not self.args.random_baseline: ###BURN IN PERIOD
print('INSIDE GRAD DESCENT')
for learner in self.portfolio:
learner.update_parameters(
self.replay_buffer, self.args.batch_size,
int(self.gen_frames * self.args.gradperstep))
self.gen_frames = 0
else:
print('BURN IN PERIOD')
gen_best = -float('inf')
gen_best_speedup = -float("inf")
gen_champ = None
########## SOFT -JOIN ROLLOUTS FOR EVO POPULATION ############
if self.args.pop_size >= 1:
for i in range(self.args.pop_size):
if self.args.use_mp:
entry = self.evo_result_pipes[i][1].recv()
else:
entry = rollout_function(
i,
'evo',
self.population[i],
self.env,
store_data=self.args.rollout_size > 0)
self.gen_frames += entry[2]
self.total_frames += entry[2]
speedup = entry[3][0]
score = entry[1]
net = self.population[entry[0]]
net.fitness_stats['speedup'] = speedup
net.fitness_stats['score'] = score
net.fitness_stats['shaped'][:] = entry[5]
self.population[entry[0]] = net
self.test_tracker.update([score, speedup], self.total_frames)
self.time_tracker.update([score, speedup],
time.time() - time_start)
if speedup > self.best_speedup:
self.best_speedup = speedup
if score > gen_best:
gen_best = score
gen_champ = self.population[i]
if speedup > gen_best_speedup:
gen_best_speedup = speedup
if score > self.best_score:
self.best_score = score
champ_index = i
self.champ_type = net.model_type
try:
torch.save(
self.population[champ_index].state_dict(),
self.args.models_folder + 'bestChamp_' +
self.args.savetag)
except:
None
# TODO
print("Best Evo Champ saved with score", '%.2f' % score)
if self.args.rollout_size > 0:
self.replay_buffer.add(entry[4])
self.evo_flag[i] = True
try:
torch.save(
gen_champ.state_dict(),
self.args.models_folder + 'genChamp_' + str(gen) +
'_speedup_' + str(gen_best_speedup) + '_' + self.args.savetag)
except:
None
############################# GENERALIZATION EXPERIMENTS ########################
_, resnet50_score, _, resnet50_speedup, _, _ = rollout_function(
0, 'evo', gen_champ, self.test_envs[0], store_data=False)
_, resnet101_score, _, resnet101_speedup, _, _ = rollout_function(
0, 'evo', gen_champ, self.test_envs[1], store_data=False)
resnet50_speedup = resnet50_speedup[0]
resnet101_speedup = resnet101_speedup[0]
self.r50_tracker.update([resnet50_score, resnet50_speedup], gen)
self.r101_tracker.update([resnet101_score, resnet101_speedup], gen)
self.r50_frames_tracker.update([resnet50_score, resnet50_speedup],
self.total_frames)
self.r101_frames_tracker.update([resnet101_score, resnet101_speedup],
self.total_frames)
bert_speedup, bert_score = None, None
if self.platform != 'wpa':
_, bert_score, _, bert_speedup, _, _ = rollout_function(
0, 'evo', gen_champ, self.test_envs[2], store_data=False)
bert_speedup = bert_speedup[0]
self.bert_tracker.update([bert_score, bert_speedup], gen)
self.bert_frames_tracker.update([bert_score, bert_speedup],
self.total_frames)
############################# GENERALIZATION EXPERIMENTS ########################
########## HARD -JOIN ROLLOUTS FOR LEARNER ROLLOUTS ############
if self.args.rollout_size > 0:
for i in range(self.args.rollout_size):
#NOISY PG
if self.args.use_mp:
entry = self.result_pipes[i][1].recv()
else:
entry = rollout_function(i,
'pg',
self.rollout_bucket[i],
self.env,
store_data=True)
learner_id = entry[0]
fitness = entry[1]
num_frames = entry[2]
speedup = entry[3][0]
self.portfolio[learner_id].update_stats(fitness, num_frames)
self.replay_buffer.add(entry[4])
self.test_tracker.update([fitness, speedup], self.total_frames)
self.time_tracker.update([fitness, speedup],
time.time() - time_start)
gen_best = max(fitness, gen_best)
self.best_speedup = max(speedup, self.best_speedup)
gen_best_speedup = max(speedup, gen_best_speedup)
self.gen_frames += num_frames
self.total_frames += num_frames
if fitness > self.best_score:
self.best_score = fitness
torch.save(
self.rollout_bucket[i].state_dict(),
self.args.models_folder + 'noisy_bestPG_' +
str(speedup) + '_' + self.args.savetag)
print("Best Rollout Champ saved with score",
'%.2f' % fitness)
noisy_speedup = speedup
# Clean PG Measurement
entry = rollout_function(i,
'evo',
self.rollout_bucket[i],
self.env,
store_data=True)
learner_id = entry[0]
fitness = entry[1]
num_frames = entry[2]
speedup = entry[3][0]
self.portfolio[learner_id].update_stats(fitness, num_frames)
self.replay_buffer.add(entry[4])
self.test_tracker.update([fitness, speedup], self.total_frames)
self.time_tracker.update([fitness, speedup],
time.time() - time_start)
gen_best = max(fitness, gen_best)
self.best_speedup = max(speedup, self.best_speedup)
gen_best_speedup = max(speedup, gen_best_speedup)
self.gen_frames += num_frames
self.total_frames += num_frames
if fitness > self.best_score:
self.best_score = fitness
torch.save(
self.rollout_bucket[i].state_dict(),
self.args.models_folder + 'clean_bestPG_' +
str(speedup) + '_' + self.args.savetag)
print("Best Clean Evo Champ saved with score",
'%.2f' % fitness)
self.pg_tracker.update([noisy_speedup, speedup],
self.total_frames)
self.roll_flag[i] = True
self.champ_tracker.update([gen_best, gen_best_speedup],
self.total_frames)
#NeuroEvolution's probabilistic selection and recombination step
if self.args.pop_size >= 1 and not self.args.random_baseline:
if gen % 1 == 0:
self.population = self.evolver.epoch(self.population,
self.rollout_bucket)
else:
self.population = self.evolver.epoch(self.population, [])
if self.evolver.selection_stats['total'] > 0:
selection_rate = (
1.0 * self.evolver.selection_stats['selected'] +
self.evolver.selection_stats['elite']
) / self.evolver.selection_stats['total']
elite_rate = selection_rate = (
1.0 * self.evolver.selection_stats['elite']
) / self.evolver.selection_stats['total']
self.migration_tracker.update([selection_rate, elite_rate],
self.total_frames)
if gen % 1 == 0:
self.checkpoint()
return gen_best
def train(self, frame_limit):
time_start = time.time()
for gen in range(1, 1000000000): # Infinite generations
# Train one iteration
gen_best = self.forward_generation(gen, time_start)
print()
print('Gen/Frames', gen, '/', self.total_frames, 'Gen_Score',
'%.2f' % gen_best, 'Best_Score', '%.2f' % self.best_score,
' Speedup', '%.2f' % self.best_speedup, ' Frames/sec:',
'%.2f' % (self.total_frames / (time.time() - time_start)),
'Buffer', self.replay_buffer.__len__(), 'Savetag',
self.args.savetag)
for net in self.population:
print(net.model_type, net.fitness_stats)
if net.model_type == 'BoltzmanChromosome':
print(net.temperature_stats)
print()
print()
try:
print('Initial Ratio', self.args.ratio, 'Current Ratio',
self.evolver.ratio, 'Chamption Type', self.champ_type)
except:
None
if gen % 5 == 0:
print('Learner Fitness', [
utils.pprint(learner.value) for learner in self.portfolio
])
if self.total_frames > frame_limit:
break
###Kill all processes
try:
for p in self.task_pipes:
p[0].send('TERMINATE')
for p in self.test_task_pipes:
p[0].send('TERMINATE')
for p in self.evo_task_pipes:
p[0].send('TERMINATE')
except:
None
def compute_policy_type(self):
if self.args.algo == 'ddqn':
return 'DDQN'
elif self.args.algo == 'sac':
return 'Gaussian_FF'
elif self.args.algo == 'td3':
return 'Deterministic_FF'
elif self.args.algo == 'sac_discrete':
return 'GumbelPolicy'
class ERL_Trainer:
def __init__(self, args, model_constructor, env_constructor):
self.args = args
self.policy_string = 'CategoricalPolicy' if env_constructor.is_discrete else 'Gaussian_FF'
self.manager = Manager()
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
#Evolution
self.evolver = SSNE(self.args)
#Initialize population
self.population = self.manager.list()
for _ in range(args.pop_size):
self.population.append(
model_constructor.make_model(self.policy_string))
#Save best policy
self.best_policy = model_constructor.make_model(self.policy_string)
#PG Learner
if env_constructor.is_discrete:
from algos.ddqn import DDQN
self.learner = DDQN(args, model_constructor)
else:
from algos.sac import SAC
self.learner = SAC(args, model_constructor)
#Replay Buffer
self.replay_buffer = Buffer(args.buffer_size)
#Initialize Rollout Bucket
self.rollout_bucket = self.manager.list()
for _ in range(args.rollout_size):
self.rollout_bucket.append(
model_constructor.make_model(self.policy_string))
############## MULTIPROCESSING TOOLS ###################
#Evolutionary population Rollout workers
self.evo_task_pipes = [Pipe() for _ in range(args.pop_size)]
self.evo_result_pipes = [Pipe() for _ in range(args.pop_size)]
self.evo_workers = [
Process(target=rollout_worker,
args=(id, 'evo', self.evo_task_pipes[id][1],
self.evo_result_pipes[id][0], args.rollout_size > 0,
self.population, env_constructor))
for id in range(args.pop_size)
]
for worker in self.evo_workers:
worker.start()
self.evo_flag = [True for _ in range(args.pop_size)]
#Learner rollout workers
self.task_pipes = [Pipe() for _ in range(args.rollout_size)]
self.result_pipes = [Pipe() for _ in range(args.rollout_size)]
self.workers = [
Process(target=rollout_worker,
args=(id, 'pg', self.task_pipes[id][1],
self.result_pipes[id][0], True, self.rollout_bucket,
env_constructor)) for id in range(args.rollout_size)
]
for worker in self.workers:
worker.start()
self.roll_flag = [True for _ in range(args.rollout_size)]
#Test bucket
self.test_bucket = self.manager.list()
self.test_bucket.append(
model_constructor.make_model(self.policy_string))
# Test workers
self.test_task_pipes = [Pipe() for _ in range(args.num_test)]
self.test_result_pipes = [Pipe() for _ in range(args.num_test)]
self.test_workers = [
Process(target=rollout_worker,
args=(id, 'test', self.test_task_pipes[id][1],
self.test_result_pipes[id][0], False,
self.test_bucket, env_constructor))
for id in range(args.num_test)
]
for worker in self.test_workers:
worker.start()
self.test_flag = False
#Trackers
self.best_score = -float('inf')
self.gen_frames = 0
self.total_frames = 0
self.test_score = None
self.test_std = None
def forward_generation(self, gen, tracker):
gen_max = -float('inf')
#Start Evolution rollouts
if self.args.pop_size > 1:
for id, actor in enumerate(self.population):
self.evo_task_pipes[id][0].send(id)
#Sync all learners actor to cpu (rollout) actor and start their rollout
self.learner.actor.cpu()
for rollout_id in range(len(self.rollout_bucket)):
utils.hard_update(self.rollout_bucket[rollout_id],
self.learner.actor)
self.task_pipes[rollout_id][0].send(0)
self.learner.actor.to(device=self.device)
#Start Test rollouts
if gen % self.args.test_frequency == 0:
self.test_flag = True
for pipe in self.test_task_pipes:
pipe[0].send(0)
############# UPDATE PARAMS USING GRADIENT DESCENT ##########
if self.replay_buffer.__len__(
) > self.args.learning_start: ###BURN IN PERIOD
for _ in range(int(self.gen_frames * self.args.gradperstep)):
s, ns, a, r, done = self.replay_buffer.sample(
self.args.batch_size)
self.learner.update_parameters(s, ns, a, r, done)
self.gen_frames = 0
########## JOIN ROLLOUTS FOR EVO POPULATION ############
all_fitness = []
all_eplens = []
if self.args.pop_size > 1:
for i in range(self.args.pop_size):
_, fitness, frames, trajectory = self.evo_result_pipes[i][
1].recv()
all_fitness.append(fitness)
all_eplens.append(frames)
self.gen_frames += frames
self.total_frames += frames
self.replay_buffer.add(trajectory)
self.best_score = max(self.best_score, fitness)
gen_max = max(gen_max, fitness)
########## JOIN ROLLOUTS FOR LEARNER ROLLOUTS ############
rollout_fitness = []
rollout_eplens = []
if self.args.rollout_size > 0:
for i in range(self.args.rollout_size):
_, fitness, pg_frames, trajectory = self.result_pipes[i][
1].recv()
self.replay_buffer.add(trajectory)
self.gen_frames += pg_frames
self.total_frames += pg_frames
self.best_score = max(self.best_score, fitness)
gen_max = max(gen_max, fitness)
rollout_fitness.append(fitness)
rollout_eplens.append(pg_frames)
######################### END OF PARALLEL ROLLOUTS ################
############ FIGURE OUT THE CHAMP POLICY AND SYNC IT TO TEST #############
if self.args.pop_size > 1:
champ_index = all_fitness.index(max(all_fitness))
utils.hard_update(self.test_bucket[0],
self.population[champ_index])
if max(all_fitness) > self.best_score:
self.best_score = max(all_fitness)
utils.hard_update(self.best_policy,
self.population[champ_index])
torch.save(self.population[champ_index].state_dict(),
self.args.aux_folder + '_best' + self.args.savetag)
print("Best policy saved with score",
'%.2f' % max(all_fitness))
else: #If there is no population, champion is just the actor from policy gradient learner
utils.hard_update(self.test_bucket[0], self.rollout_bucket[0])
###### TEST SCORE ######
if self.test_flag:
self.test_flag = False
test_scores = []
for pipe in self.test_result_pipes: #Collect all results
_, fitness, _, _ = pipe[1].recv()
self.best_score = max(self.best_score, fitness)
gen_max = max(gen_max, fitness)
test_scores.append(fitness)
test_scores = np.array(test_scores)
test_mean = np.mean(test_scores)
test_std = (np.std(test_scores))
tracker.update([test_mean], self.total_frames)
else:
test_mean, test_std = None, None
#NeuroEvolution's probabilistic selection and recombination step
if self.args.pop_size > 1:
self.evolver.epoch(gen, self.population, all_fitness,
self.rollout_bucket)
#Compute the champion's eplen
champ_len = all_eplens[all_fitness.index(
max(all_fitness))] if self.args.pop_size > 1 else rollout_eplens[
rollout_fitness.index(max(rollout_fitness))]
return gen_max, champ_len, all_eplens, test_mean, test_std, rollout_fitness, rollout_eplens
def train(self, frame_limit):
# Define Tracker class to track scores
test_tracker = utils.Tracker(self.args.savefolder,
['score_' + self.args.savetag],
'.csv') # Tracker class to log progress
time_start = time.time()
for gen in range(1, 1000000000): # Infinite generations
# Train one iteration
max_fitness, champ_len, all_eplens, test_mean, test_std, rollout_fitness, rollout_eplens = self.forward_generation(
gen, test_tracker)
if test_mean:
self.args.writer.add_scalar('test_score', test_mean, gen)
print(
'Gen/Frames:', gen, '/', self.total_frames, ' Gen_max_score:',
'%.2f' % max_fitness, ' Champ_len', '%.2f' % champ_len,
' Test_score u/std', utils.pprint(test_mean),
utils.pprint(test_std), ' Rollout_u/std:',
utils.pprint(np.mean(np.array(rollout_fitness))),
utils.pprint(np.std(np.array(rollout_fitness))),
' Rollout_mean_eplen:',
utils.pprint(sum(rollout_eplens) /
len(rollout_eplens)) if rollout_eplens else None)
if gen % 5 == 0:
print(
'Best_score_ever:'
'/', '%.2f' % self.best_score, ' FPS:',
'%.2f' % (self.total_frames / (time.time() - time_start)),
'savetag', self.args.savetag)
print()
if self.total_frames > frame_limit:
break
###Kill all processes
try:
for p in self.task_pipes:
p[0].send('TERMINATE')
for p in self.test_task_pipes:
p[0].send('TERMINATE')
for p in self.evo_task_pipes:
p[0].send('TERMINATE')
except:
None
class CERL_Trainer:
"""Main CERL class containing all methods for CERL
Parameters:
args (object): Parameter class with all the parameters
"""
def __init__(self, args, model_constructor, env_constructor):
self.args = args
self.policy_string = self.compute_policy_type()
#Evolution
self.evolver = SSNE(self.args)
#MP TOOLS
self.manager = Manager()
#Genealogy tool
self.genealogy = Genealogy()
#Initialize population
self.population = self.manager.list()
seed = True
for _ in range(args.pop_size):
self.population.append(
model_constructor.make_model(self.policy_string, seed=seed))
seed = False
#SEED
#self.population[0].load_state_dict(torch.load('Results/Auxiliary/_bestcerl_td3_s2019_roll10_pop10_portfolio10'))
#Save best policy
self.best_policy = model_constructor.make_model(self.policy_string)
#Turn off gradients and put in eval mod
for actor in self.population:
actor = actor.cpu()
actor.eval()
#Init BUFFER
self.replay_buffer = Buffer(args.buffer_size)
self.data_bucket = self.replay_buffer.tuples
#Intialize portfolio of learners
self.portfolio = []
self.portfolio = initialize_portfolio(self.portfolio, self.args,
self.genealogy,
args.portfolio_id,
model_constructor)
#Initialize Rollout Bucket
self.rollout_bucket = self.manager.list()
for _ in range(len(self.portfolio)):
self.rollout_bucket.append(
model_constructor.make_model(self.policy_string))
############## MULTIPROCESSING TOOLS ###################
#Evolutionary population Rollout workers
self.evo_task_pipes = [Pipe() for _ in range(args.pop_size)]
self.evo_result_pipes = [Pipe() for _ in range(args.pop_size)]
self.evo_workers = [
Process(target=rollout_worker,
args=(id, 'evo', self.evo_task_pipes[id][1],
self.evo_result_pipes[id][0], self.data_bucket,
self.population, env_constructor))
for id in range(args.pop_size)
]
for worker in self.evo_workers:
worker.start()
self.evo_flag = [True for _ in range(args.pop_size)]
#Learner rollout workers
self.task_pipes = [Pipe() for _ in range(args.rollout_size)]
self.result_pipes = [Pipe() for _ in range(args.rollout_size)]
self.workers = [
Process(target=rollout_worker,
args=(id, 'pg', self.task_pipes[id][1],
self.result_pipes[id][0], self.data_bucket,
self.rollout_bucket, env_constructor))
for id in range(args.rollout_size)
]
for worker in self.workers:
worker.start()
self.roll_flag = [True for _ in range(args.rollout_size)]
#Test bucket
self.test_bucket = self.manager.list()
self.test_bucket.append(
model_constructor.make_model(self.policy_string))
#5 Test workers
self.test_task_pipes = [
Pipe() for _ in range(env_constructor.dummy_env.test_size)
]
self.test_result_pipes = [
Pipe() for _ in range(env_constructor.dummy_env.test_size)
]
self.test_workers = [
Process(target=rollout_worker,
args=(id, 'test', self.test_task_pipes[id][1],
self.test_result_pipes[id][0], None,
self.test_bucket, env_constructor))
for id in range(env_constructor.dummy_env.test_size)
]
for worker in self.test_workers:
worker.start()
self.test_flag = False
#Meta-learning controller (Resource Distribution)
self.allocation = [
] #Allocation controls the resource allocation across learners
for i in range(args.rollout_size):
self.allocation.append(
i % len(self.portfolio)) #Start uniformly (equal resources)
#Trackers
self.best_score = 0.0
self.gen_frames = 0
self.total_frames = 0
self.test_score = None
self.test_std = None
self.best_r1_score = 0.0
self.ep_len = 0
self.r1_reward = 0
self.num_footsteps = 0
self.test_trace = []
def checkpoint(self):
utils.pickle_obj(
self.args.aux_folder + self.args.algo + '_checkpoint_frames' +
str(self.total_frames), self.portfolio)
def load_checkpoint(self, filename):
self.portfolio = utils.unpickle_obj(filename)
def forward_generation(self, gen, tracker):
"""Main training loop to do rollouts, neureoevolution, and policy gradients
Parameters:
gen (int): Current epoch of training
Returns:
None
"""
################ START ROLLOUTS ##############
#Start Evolution rollouts
if self.args.pop_size > 1:
for id, actor in enumerate(self.population):
if self.evo_flag[id]:
self.evo_task_pipes[id][0].send(id)
self.evo_flag[id] = False
#Sync all learners actor to cpu (rollout) actor
for i, learner in enumerate(self.portfolio):
learner.algo.actor.cpu()
utils.hard_update(self.rollout_bucket[i], learner.algo.actor)
learner.algo.actor.cuda()
# Start Learner rollouts
for rollout_id, learner_id in enumerate(self.allocation):
if self.roll_flag[rollout_id]:
self.task_pipes[rollout_id][0].send(learner_id)
self.roll_flag[rollout_id] = False
#Start Test rollouts
if gen % 5 == 0:
self.test_flag = True
for pipe in self.test_task_pipes:
pipe[0].send(0)
############# UPDATE PARAMS USING GRADIENT DESCENT ##########
if self.replay_buffer.__len__(
) > self.args.learning_start: ###BURN IN PERIOD
#Spin up threads for each learner
threads = [
threading.Thread(
target=learner.update_parameters,
args=(self.replay_buffer, self.args.batch_size,
int(self.gen_frames * self.args.gradperstep)))
for learner in self.portfolio
]
# Start threads
for thread in threads:
thread.start()
#Join threads
for thread in threads:
thread.join()
self.gen_frames = 0
########## SOFT -JOIN ROLLOUTS FOR EVO POPULATION ############
if self.args.pop_size > 1:
all_fitness = []
all_net_ids = []
all_eplens = []
while True:
for i in range(self.args.pop_size):
if self.evo_result_pipes[i][1].poll():
entry = self.evo_result_pipes[i][1].recv()
all_fitness.append(entry[1])
all_net_ids.append(entry[0])
all_eplens.append(entry[2])
self.gen_frames += entry[2]
self.total_frames += entry[2]
self.evo_flag[i] = True
# Soft-join (50%)
if len(all_fitness
) / self.args.pop_size >= self.args.asynch_frac:
break
########## HARD -JOIN ROLLOUTS FOR LEARNER ROLLOUTS ############
if self.args.rollout_size > 0:
for i in range(self.args.rollout_size):
entry = self.result_pipes[i][1].recv()
learner_id = entry[0]
fitness = entry[1]
num_frames = entry[2]
self.portfolio[learner_id].update_stats(fitness, num_frames)
self.gen_frames += num_frames
self.total_frames += num_frames
if fitness > self.best_score: self.best_score = fitness
self.roll_flag[i] = True
######################### END OF PARALLEL ROLLOUTS ################
############ PROCESS MAX FITNESS #############
if self.args.pop_size > 1:
champ_index = all_net_ids[all_fitness.index(max(all_fitness))]
utils.hard_update(self.test_bucket[0],
self.population[champ_index])
else: #Run PG in isolation
utils.hard_update(self.test_bucket[0], self.rollout_bucket[0])
###### TEST SCORE ######
if self.test_flag:
self.test_flag = False
test_scores = []
eplens = []
r1_reward = []
num_footsteps = []
for pipe in self.test_result_pipes: #Collect all results
entry = pipe[1].recv()
test_scores.append(entry[1])
eplens.append(entry[3])
r1_reward.append(entry[4])
num_footsteps.append(entry[5])
test_scores = np.array(test_scores)
test_mean = np.mean(test_scores)
test_std = (np.std(test_scores))
self.test_trace.append(test_mean)
self.num_footsteps = np.mean(np.array(num_footsteps))
self.ep_len = np.mean(np.array(eplens))
self.r1_reward = np.mean(np.array(r1_reward))
if self.r1_reward > self.best_r1_score:
self.best_r1_score = self.r1_reward
utils.hard_update(self.best_policy, self.test_bucket[0])
torch.save(
self.test_bucket[0].state_dict(),
self.args.aux_folder + '_bestR1_' + self.args.savetag)
print("Best R2 policy saved with score",
'%.2f' % self.r1_reward)
if test_mean > self.best_score:
self.best_score = test_mean
utils.hard_update(self.best_policy, self.test_bucket[0])
torch.save(
self.test_bucket[0].state_dict(),
self.args.aux_folder + '_bestShaped' + self.args.savetag)
print("Best Shaped policy saved with score",
'%.2f' % test_mean)
tracker.update([test_mean, self.r1_reward], self.total_frames)
else:
test_mean, test_std = None, None
# Referesh buffer (housekeeping tasks - pruning to keep under capacity)
self.replay_buffer.referesh()
#NeuroEvolution's probabilistic selection and recombination step
if self.args.pop_size > 1:
if self.args.scheme == 'multipoint':
sample_size = self.args.batch_size if self.replay_buffer.__len__(
) >= self.args.batch_size else self.replay_buffer.__len__()
states, _, _, _, _ = self.replay_buffer.sample(
batch_size=sample_size)
else:
states = None
self.evolver.epoch(self.population, all_net_ids, all_fitness,
self.rollout_bucket, states)
#META LEARNING - RESET ALLOCATION USING UCB
if self.args.rollout_size > 0:
self.allocation = ucb(len(self.allocation), self.portfolio,
self.args.ucb_coefficient)
#Metrics
if self.args.pop_size > 1:
champ_len = all_eplens[all_fitness.index(max(all_fitness))]
#champ_wwid = int(self.pop[champ_index].wwid.item())
max_fit = max(all_fitness)
else:
champ_len = num_frames
all_fitness = [fitness]
max_fit = fitness
all_eplens = [num_frames]
return max_fit, champ_len, all_eplens, test_mean, test_std
def train(self, frame_limit):
# Define Tracker class to track scores
test_tracker = utils.Tracker(
self.args.savefolder,
['score_' + self.args.savetag, 'r2_' + self.args.savetag],
'.csv') # Tracker class to log progress
grad_temp = [
str(i) + 'entropy_' + self.args.savetag
for i in range(len(self.portfolio))
] + [
str(i) + 'policyQ_' + self.args.savetag
for i in range(len(self.portfolio))
]
grad_tracker = utils.Tracker(self.args.aux_folder, grad_temp,
'.csv') # Tracker class to log progress
time_start = time.time()
for gen in range(1, 1000000000): # Infinite generations
# Train one iteration
max_fitness, champ_len, all_eplens, test_mean, test_std = self.forward_generation(
gen, test_tracker)
print('Gen/Frames', gen, '/', self.total_frames,
' Pop_max/max_ever:', '%.2f' % max_fitness, '/',
'%.2f' % self.best_score, ' Avg:',
'%.2f' % test_tracker.all_tracker[0][1], ' Frames/sec:',
'%.2f' % (self.total_frames / (time.time() - time_start)),
' Champ_len', '%.2f' % champ_len, ' Test_score u/std',
utils.pprint(test_mean), utils.pprint(test_std), 'Ep_len',
'%.2f' % self.ep_len, '#Footsteps',
'%.2f' % self.num_footsteps, 'R2_Reward',
'%.2f' % self.r1_reward, 'savetag', self.args.savetag)
grad_temp = [
algo.algo.entropy['mean'] for algo in self.portfolio
] + [algo.algo.policy_q['mean'] for algo in self.portfolio]
grad_tracker.update(grad_temp, self.total_frames)
if gen % 5 == 0:
print('Learner Fitness', [
utils.pprint(learner.value) for learner in self.portfolio
], 'Sum_stats_resource_allocation',
[learner.visit_count for learner in self.portfolio])
try:
print('Entropy', [
'%.2f' % algo.algo.entropy['mean']
for algo in self.portfolio
], 'Next_Entropy', [
'%.2f' % algo.algo.next_entropy['mean']
for algo in self.portfolio
], 'Poilcy_Q', [
'%.2f' % algo.algo.policy_q['mean']
for algo in self.portfolio
], 'Critic_Loss', [
'%.2f' % algo.algo.critic_loss['mean']
for algo in self.portfolio
])
print()
except:
None
if self.total_frames > frame_limit:
break
###Kill all processes
try:
for p in self.task_pipes:
p[0].send('TERMINATE')
for p in self.test_task_pipes:
p[0].send('TERMINATE')
for p in self.evo_task_pipes:
p[0].send('TERMINATE')
except:
None
def compute_policy_type(self):
if self.args.algo == 'ddqn':
return 'DDQN'
elif self.args.algo == 'sac':
return 'Gaussian_FF'
elif self.args.algo == 'td3':
return 'Deterministic_FF'
class Evaluator(object):
def __init__(
self,
CERL_agent,
num_workers,
trainers,
pomdp_adv=False
): #trainers first is the blue agent and second is the red model
self.num_workers = num_workers
self.trainers = trainers
self.pomdp_adv = pomdp_adv
self.args = CERL_agent.args
self.drqn = CERL_agent.args.drqn #denote if blue uses drqn
if self.pomdp_adv:
self.trainers = [trainers[0],
None] #make sure the red model is never used
self.buffer_gpu = CERL_agent.args.buffer_gpu
self.batch_size = CERL_agent.args.batch_size
self.algo = CERL_agent.args.algo
self.state_dim = CERL_agent.args.state_dim
self.action_dim = CERL_agent.args.action_dim
self.buffer = Buffer(BUFFER_SIZE,
self.buffer_gpu) #initialize own replay buffer
self.data_bucket = self.buffer.tuples
self.evo_task_pipes = [Pipe() for _ in range(self.num_workers)]
self.evo_result_pipes = [Pipe() for _ in range(self.num_workers)]
self.actual_red_worker = Actor(
CERL_agent.args.state_dim, CERL_agent.args.action_dim, -1,
'dis') #this model is shared accross the workers
self.actual_red_worker.share_memory()
self.td3args = {
'policy_noise': 0.2,
'policy_noise_clip': 0.5,
'policy_ups_freq': 2,
'action_low': CERL_agent.args.action_low,
'action_high': CERL_agent.args.action_high,
'cerl_args': self.args
}
self.renew_learner(
) #now we are not using new learner for each iteration
self.rollout_bucket = [
self.actual_red_worker for i in range(num_workers)
]
self.workers = [
Process(target=rollout_worker,
args=(id, 3, self.evo_task_pipes[id][1],
self.evo_result_pipes[id][0], False,
self.data_bucket, self.rollout_bucket, 'dummy_name',
None, 'dis', self.trainers, False, self.pomdp_adv))
for id in range(num_workers)
]
for worker in self.workers:
worker.start()
self.evo_flag = [True for _ in range(self.num_workers)]
#def initialize(self, actor_in): #use the given actor parameter to initialize the red actor
# utils.hard_update(self.actual_red_actor, actor_in)
def renew_learner(
self
): #create a new learning agent, with randomized initial parameter
self.learner = Learner(-1,
self.algo,
self.state_dim,
self.action_dim,
actor_lr=5e-5,
critic_lr=1e-3,
gamma=0.99,
tau=5e-3,
init_w=True,
**self.td3args)
self.actual_red_actor = self.learner.algo.actor
def collect_trajectory(self):
utils.hard_update(self.actual_red_worker,
self.actual_red_actor) #first snyc the actor
#launch rollout_workers
for id, actor in enumerate(self.rollout_bucket):
if self.evo_flag[id]:
self.evo_task_pipes[id][0].send(
(id, 0)) #second argument in send is dummy
self.evo_flag[id] = False
#wait for the rollout to complete and record fitness
all_fitness = []
for i in range(self.num_workers):
entry = self.evo_result_pipes[i][1].recv()
all_fitness.append(entry[1])
self.evo_flag[i] = True
self.buffer.referesh() #update replay buffer
return all_fitness
def train_red(
self, training_iterations
): #alternate between collect_trajectory and parameter update
while self.buffer.__len__() < self.batch_size * 10: ###BURN IN PERIOD
self.collect_trajectory()
for i in range(training_iterations):
self.collect_trajectory()
self.buffer.tensorify() # Tensorify the buffer for fast sampling
self.learner.update_parameters(self.buffer, self.buffer_gpu,
self.batch_size, 2) #2 update steps
def evaluate(
self
): #evaluate the quality of blue agent policy, by training a red against it, after evaluation, erase the reply buffer and renew learner
self.train_red(TRAIN_ITERATION)
self.clear_buffer()
#self.renew_learner()
return self.evaluate_fixed_agents(
self.trainers[0], self.trainers[1],
[self.actual_red_actor
]) #calculate the mean and std of the evaluation metric
def evaluate_fixed_agents(
self,
blue_dqn,
red_model,
red_actor_list,
num_iterations=25
): #evaluate the performance given agents, use random neutral and red agent
if self.algo == 'dis': # make env with blue and red policy agent inside,
dis_env = make_self_play_env(
seed=0,
return_policy_agent=False,
trainers=[blue_dqn, red_model]
)[0] # trainer if not None, first is the shared DQN agent, second is the best red policy
env = EnvironmentWrapper(
'', self.algo, dis_env,
0) # the "0" is the index for training blue agent
elif self.algo == 'TD3_tennis':
tennis_env = make_tennis_env.TennisEnvFactory(
seed=np.random.choice(np.array(range(len(self.pop)))),
no_graphics=True,
pid=-1).getEnv()[0]
env = EnvironmentWrapper('Tennis', self.algo, tennis_env, 0)
else:
raise Exception("only work for 'dis' envir?")
average_reward = 0
eps = 0
average_red_reward = 0
red_count = 0
average_actual_blue_reward = 0
blue_count = 0
belief_and_true_type_list = []
assert len(red_actor_list
) is not None, "make sure to input a list of possible red"
for it in range(num_iterations):
belief_and_true_type = []
if not self.pomdp_adv: # if pomdp_adv, make sure that TD3_actor is never used
red_actor = random.choice(red_actor_list)
env.set_TD3_actor(red_actor)
fitness = 0.0
# here fitness if simplely reward
state = env.reset()
belief_and_true_type.append(env.belief_and_true_type())
env.randomize_neu_adv()
if self.pomdp_adv:
env.try_set_pomdp_adv(
) # try to set if opponent to pomdp adv if opponent is adversary, else do nothing
render_flag = (np.random.random() < 0.05)
while True: # unless done
action = blue_dqn.act(state, eps=eps)
next_state, reward, done, info = env.step(
copy.deepcopy(action), use_actual_reward=self.drqn)
belief_and_true_type.append(env.belief_and_true_type())
if render_flag and self.args.render:
env.render()
state = next_state
fitness += reward
if done:
average_red_reward += env.get_red_reward(
) if env.get_red_reward() is not None else 0
average_actual_blue_reward += env.get_blue_actual_reward(
) if env.get_blue_actual_reward() is not None else 0
red_count += 1 if env.get_red_reward() is not None else 0
blue_count += 1 if env.get_blue_actual_reward(
) is not None else 0
if render_flag: env.env.close()
break
belief_and_true_type_list.append(belief_and_true_type)
average_reward += fitness
average_reward /= num_iterations
if red_count != 0:
average_red_reward /= red_count
if blue_count != 0:
average_actual_blue_reward /= blue_count
return average_reward, average_red_reward, average_actual_blue_reward, belief_and_true_type_list
def clear_buffer(self):
self.buffer.clear_buffer_data() #reinitialize replay buffer
def kill_processes(self):
for id, actor in enumerate(self.rollout_bucket):
self.evo_task_pipes[id][0].send(
('TERMINATE', 0)) #second argument in send is dummy
def __del__(self):
self.kill_processes()
