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 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 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 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'
