def main(args):
continuous_actions = True
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_folder = './saves/{0}'.format(args.output_folder)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
with open(os.path.join(save_folder, 'config.json'), 'w') as f:
config = {k: v for (k, v) in vars(args).items() if k != 'device'}
config.update(device=args.device.type)
json.dump(config, f, indent=2)
sampler = BatchSampler(args.env_name,
batch_size=args.fast_batch_size,
num_workers=args.num_workers)
if continuous_actions:
policy = NormalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
int(np.prod(sampler.envs.action_space.shape)),
hidden_sizes=(args.hidden_size, ) * args.num_layers)
else:
policy = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size, ) * args.num_layers)
baseline = LinearFeatureBaseline(
int(np.prod(sampler.envs.observation_space.shape)))
metalearner = MetaLearner(sampler,
policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device)
for batch in range(args.num_batches):
print("========== BATCH NUMBER {0} ==========".format(batch))
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
episodes = metalearner.sample(tasks, first_order=args.first_order)
metalearner.step(episodes,
max_kl=args.max_kl,
cg_iters=args.cg_iters,
cg_damping=args.cg_damping,
ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
# Tensorboard
writer.add_scalar('total_rewards/before_update',
total_rewards([ep.rewards for ep, _ in episodes]),
batch)
writer.add_scalar('total_rewards/after_update',
total_rewards([ep.rewards for _, ep in episodes]),
batch)
# Save policy network
with open(
os.path.join(save_folder, 'policy-{0}.pt'.format(batch + 256)),
'wb') as f:
torch.save(policy.state_dict(), f)
def main(args):
continuous_actions = (args.env_name in ['AntVel-v1', 'AntDir-v1',
'AntPos-v0', 'HalfCheetahVel-v1', 'HalfCheetahDir-v1',
'2DNavigation-v0'])
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_folder = './saves/{0}'.format(args.output_folder)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
with open(os.path.join(save_folder, 'config.json'), 'w') as f:
config = {k: v for (k, v) in vars(args).items() if k != 'device'}
config.update(device=args.device.type)
json.dump(config, f, indent=2)
sampler = BatchSampler(args.env_name, batch_size=args.fast_batch_size,
num_workers=args.num_workers)
if continuous_actions:
policy = NormalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
int(np.prod(sampler.envs.action_space.shape)),
hidden_sizes=(args.hidden_size,) * args.num_layers)
else:
policy = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size,) * args.num_layers)
baseline = LinearFeatureBaseline(
int(np.prod(sampler.envs.observation_space.shape)))
metalearner = MetaLearnerNGLVCVPG(sampler, policy, baseline, gamma=args.gamma,
fast_lr=args.fast_lr, tau=args.tau, device=args.device, verbose=args.verbose)
for batch in range(args.num_batches):
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
start = time.time()
episodes, kls, param_diffs = metalearner.sample(tasks, first_order=args.first_order, cg_iters=args.cg_iters)
sample_time = time.time() - start
start = time.time()
if args.optimizer is 'sgd':
metalearner.step_sgd(episodes, max_kl=args.max_kl, cg_iters=args.cg_iters,
cg_damping=args.cg_damping, ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
else:
metalearner.step_adam(episodes, max_kl=args.max_kl, cg_iters=args.cg_iters,
cg_damping=args.cg_damping, ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
update_time = time.time() - start
# Tensorboard
writer.add_scalar('total_rewards/before_update',
total_rewards([ep.rewards for ep, _ in episodes]), batch)
writer.add_scalar('total_rewards/after_update',
total_rewards([ep.rewards for _, ep in episodes]), batch)
writer.add_scalar('kl-mean between meta update',
torch.mean(torch.stack(kls)), batch)
writer.add_scalar('kl-std between meta update',
torch.std(torch.stack(kls)), batch)
writer.add_scalar('Euclidean-distance-mean between meta update',
torch.mean(torch.stack(param_diffs)), batch)
writer.add_scalar('Euclidean-distance-std between meta update',
torch.std(torch.stack(param_diffs)), batch)
print("Batch {}. before_update: {}, after_update: {}\n sample time {}, update_time {}".format(batch,
total_rewards([ep.rewards for ep, _ in episodes]),
total_rewards([ep.rewards for _, ep in episodes]), sample_time, update_time))
print("Batch {}. kl-divergence between meta update: {}, kl std: {}".format(
batch, torch.mean(torch.stack(kls)), torch.std(torch.stack(kls))))
print("Batch {}. Euclidean-distance-mean meta update: {}, Euclidean-distance-std: {}".format(
batch, torch.mean(torch.stack(param_diffs)), torch.std(torch.stack(param_diffs))))
# Save policy network
with open(os.path.join(save_folder,
'policy-{0}.pt'.format(batch)), 'wb') as f:
torch.save(policy.state_dict(), f)
def main(args):
group_name = ''.join([
random.choice(string.ascii_letters + string.digits) for n in range(4)
])
wandb.init(group=group_name, job_type='optimizer', tensorboard=True)
wandb.config.update(args)
device = torch.device(args.device)
continuous_actions = (args.env_name in [
'AntVel-v1', 'AntDir-v1', 'AntPos-v0', 'HalfCheetahVel-v1',
'HalfCheetahDir-v1', '2DNavigation-v0'
])
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_folder = './saves/{0}'.format(args.output_folder)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
with open(os.path.join(save_folder, 'config.json'), 'w') as f:
config = {k: v for (k, v) in vars(args).items() if k != 'device'}
config.update(device=device.type)
json.dump(config, f, indent=2)
sampler = BatchSampler(group_name,
args.env_name,
batch_size=args.fast_batch_size,
num_workers=args.num_workers)
if continuous_actions:
policy = NormalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
int(np.prod(sampler.envs.action_space.shape)),
hidden_sizes=(args.hidden_size, ) * args.num_layers)
else:
policy = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size, ) * args.num_layers)
baseline = LinearFeatureBaseline(
int(np.prod(sampler.envs.observation_space.shape)))
metalearner = MetaLearner(sampler,
policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=device)
for batch in range(args.num_batches):
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
episodes = metalearner.sample(tasks, first_order=args.first_order)
metalearner.step(episodes,
max_kl=args.max_kl,
cg_iters=args.cg_iters,
cg_damping=args.cg_damping,
ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
# Tensorboard
writer.add_scalar('total_rewards/before_update',
total_rewards([ep.rewards for ep, _ in episodes]),
batch)
writer.add_scalar('total_rewards/after_update',
total_rewards([ep.rewards for _, ep in episodes]),
batch)
# Save policy network
with open(os.path.join(save_folder, 'policy-{0}.pt'.format(batch)),
'wb') as f:
torch.save(policy.state_dict(), f)
def main(args):
logging.basicConfig(filename=args.debug_file,
level=logging.WARNING,
filemode='w')
logging.getLogger('metalearner').setLevel(logging.INFO)
continuous_actions = (args.env_name in [
'AntVel-v1', 'AntDir-v1', 'AntPos-v0', 'HalfCheetahVel-v1',
'HalfCheetahDir-v1', '2DNavigation-v0', 'PendulumTheta-v0'
])
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_folder = './saves/{0}'.format(args.output_folder)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
with open(os.path.join(save_folder, 'config.json'), 'w') as f:
config = {k: v for (k, v) in vars(args).items() if k != 'device'}
config.update(device=args.device.type)
json.dump(config, f, indent=2)
sampler = BatchSampler(args.env_name,
batch_size=args.fast_batch_size,
num_workers=args.num_workers)
#if args.baseline == 'critic shared':
# policy = NormalMLPPolicyA2C(int(np.prod(sampler.envs.observation_space.shape)),
# int(np.prod(sampler.envs.action_space.shape)),
# hidden_sizes=(args.hidden_size,) * args.num_layers)
if continuous_actions:
policy = NormalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
int(np.prod(sampler.envs.action_space.shape)),
hidden_sizes=(args.hidden_size, ) * args.num_layers)
else:
policy = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size, ) * args.num_layers)
if args.baseline == 'linear':
baseline = LinearFeatureBaseline(
int(np.prod(sampler.envs.observation_space.shape)))
elif args.baseline == 'critic separate':
baseline = CriticFunction(
int(np.prod(sampler.envs.observation_space.shape)),
1,
hidden_sizes=(args.hidden_size, ) * args.num_layers)
#elif args.baseline == 'critic shared':
# RANJANI TO DO
metalearner = MetaLearner(sampler,
policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device,
baseline_type=args.baseline,
cliprange=args.cliprange,
noptepochs=args.noptepochs,
usePPO=args.usePPO,
nminibatches=args.nminibatches,
ppo_lr=args.ppo_lr,
useSGD=args.useSGD,
ppo_momentum=args.ppo_momentum,
grad_clip=args.grad_clip)
for batch in range(args.num_batches):
print("*********************** Batch: " + str(batch) +
" ****************************")
print("Creating tasks...")
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
print("Creating episodes...")
episodes, grad_norm = metalearner.sample(tasks,
first_order=args.first_order)
print("Taking a meta step...")
metalearner.step(episodes,
max_kl=args.max_kl,
cg_iters=args.cg_iters,
cg_damping=args.cg_damping,
ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
print("Writing results to tensorboard...")
# Tensorboard
writer.add_scalar('total_rewards/before_update',
total_rewards([ep.rewards for ep, _ in episodes]),
batch)
writer.add_scalar('total_rewards/after_update',
total_rewards([ep.rewards for _, ep in episodes]),
batch)
if grad_norm:
writer.add_scalar('PPO mb grad norm', np.average(grad_norm))
print(np.average(grad_norm))
print("Saving policy network...")
# Save policy network
with open(os.path.join(save_folder, 'policy-{0}.pt'.format(batch)),
'wb') as f:
torch.save(policy.state_dict(), f)
print("***************************************************")
def main(args):
continuous_actions = (args.env_name in [
'AntVel-v1', 'AntDir-v1', 'AntPos-v0', 'HalfCheetahVel-v1',
'HalfCheetahDir-v1', '2DNavigation-v0'
])
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_folder = './saves/{0}'.format(args.output_folder)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
with open(os.path.join(save_folder, 'config.json'), 'w') as f:
config = {k: v for (k, v) in vars(args).items() if k != 'device'}
config.update(device=args.device.type)
json.dump(config, f, indent=2)
sampler = BatchSampler(args.env_name,
batch_size=args.fast_batch_size,
num_workers=args.num_workers)
if args.env_name == 'AntVel-v1':
param_bounds = {"goal": [0, 3]}
if args.env_name == 'AntPos-v0':
param_bounds = {"x": [-3, 3], "y": [-3, 3]}
teacher = TeacherController(args.teacher,
args.nb_test_episodes,
param_bounds,
seed=args.seed,
teacher_params={})
tree = TreeLSTM(args.tree_hidden_layer,
len(param_bounds.keys()),
args.cluster_0,
args.cluster_1,
device=args.device)
if continuous_actions:
policy = NormalMLPPolicy(int(
np.prod(sampler.envs.observation_space.shape) +
args.tree_hidden_layer),
int(np.prod(sampler.envs.action_space.shape)),
hidden_sizes=(args.hidden_size, ) *
args.num_layers,
tree=tree)
else:
policy = CategoricalMLPPolicy(int(
np.prod(sampler.envs.observation_space.shape) +
args.tree_hidden_layer),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size, ) *
args.num_layers,
tree=tree)
baseline = LinearFeatureBaseline(
int(np.prod(sampler.envs.observation_space.shape)) +
args.tree_hidden_layer)
metalearner = MetaLearner(sampler,
policy,
baseline,
tree=tree,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device)
all_tasks = []
for batch in range(args.num_batches):
print("starting iteration {}".format(batch))
tasks = []
for _ in range(args.meta_batch_size):
if args.env_name == 'AntPos-v0':
tasks.append(
{"position": teacher.task_generator.sample_task()})
if args.env_name == 'AntVel-v1':
tasks.append(
{"velocity": teacher.task_generator.sample_task()[0]})
all_tasks.append(tasks)
# tasks = np.array(tasks)
# tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
with open('./logs/{0}/task_list.pkl'.format(args.output_folder),
'wb') as pf:
pickle.dump(all_tasks, pf)
episodes = metalearner.sample(tasks, first_order=args.first_order)
metalearner.step(episodes,
max_kl=args.max_kl,
cg_iters=args.cg_iters,
cg_damping=args.cg_damping,
ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
# Tensorboard
writer.add_scalar('total_rewards/before_update',
total_rewards([ep.rewards for ep, _ in episodes]),
batch)
writer.add_scalar('total_rewards/after_update',
total_rewards([ep.rewards for _, ep in episodes]),
batch)
tr = [ep.rewards for _, ep in episodes]
tr = [torch.mean(torch.sum(rewards, dim=0)).item() for rewards in tr]
print("rewards:", tr)
for t in range(args.meta_batch_size):
if args.env_name == 'AntPos-v0':
teacher.task_generator.update(tasks[t]["position"], tr[t])
if args.env_name == 'AntVel-v1':
teacher.task_generator.update(np.array([tasks[t]["velocity"]]),
tr[t])
# Save policy network
with open(os.path.join(save_folder, 'policy-{0}.pt'.format(batch)),
'wb') as f:
torch.save(policy.state_dict(), f)
# Save tree
torch.save(tree, os.path.join(save_folder,
'tree-{0}.pt'.format(batch)))
def main(args):
continuous_actions = (args.env_name in [
'AntVel-v1', 'AntDir-v1', 'AntPos-v0', 'HalfCheetahVel-v1',
'HalfCheetahDir-v1', '2DNavigation-v0', 'RVONavigation-v0',
'RVONavigationAll-v0'
])
assert continuous_actions == True
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_folder = './saves/{0}'.format(args.output_folder)
log_traj_folder = './logs/{0}'.format(args.output_traj_folder)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
if not os.path.exists(log_traj_folder):
os.makedirs(log_traj_folder)
with open(os.path.join(save_folder, 'config.json'), 'w') as f:
config = {k: v for (k, v) in vars(args).items() if k != 'device'}
config.update(device=args.device.type)
json.dump(config, f, indent=2)
# log_reward_total_file = open('./logs/reward_total.txt', 'a')
# log_reward_dist_file = open('./logs/reward_dist.txt', 'a')
# log_reward_col_file = open('./logs/reward_col.txt', 'a')
sampler = BatchSampler(args.env_name,
batch_size=args.fast_batch_size,
num_workers=args.num_workers)
# print(sampler.envs.observation_space.shape)
# print(sampler.envs.action_space.shape)
# eewfe
if continuous_actions:
policy = NormalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
int(np.prod(sampler.envs.action_space.shape)),
hidden_sizes=(args.hidden_size, ) * args.num_layers)
else:
policy = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size, ) * args.num_layers)
# baseline = LinearFeatureBaseline(
# int(np.prod(sampler.envs.observation_space.shape)))
baseline = LinearFeatureBaseline(int(np.prod((2, ))))
resume_training = True
if resume_training:
saved_policy_path = os.path.join(
'./TrainingResults/result2//saves/{0}'.format(
'maml-2DNavigation-dir'), 'policy-180.pt')
if os.path.isfile(saved_policy_path):
print('Loading a saved policy')
policy_info = torch.load(saved_policy_path)
policy.load_state_dict(policy_info)
else:
sys.exit("The requested policy does not exist for loading")
metalearner = MetaLearner(sampler,
policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device)
start_time = time.time()
for batch in range(args.num_batches):
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
episodes = metalearner.sample(tasks, first_order=args.first_order)
metalearner.step(episodes,
max_kl=args.max_kl,
cg_iters=args.cg_iters,
cg_damping=args.cg_damping,
ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
# print("observations shape: ")
# print(episodes[0][1].observations.shape)
# ewerw
# Tensorboard
total_reward_be, dist_reward_be, col_reward_be = total_rewards(
[ep.rewards for ep, _ in episodes])
total_reward_af, dist_reward_af, col_reward_af = total_rewards(
[ep.rewards for _, ep in episodes])
log_reward_total_file = open('./logs/reward_total.txt', 'a')
log_reward_dist_file = open('./logs/reward_dist.txt', 'a')
log_reward_col_file = open('./logs/reward_col.txt', 'a')
log_reward_total_file.write(
str(batch) + ',' + str(total_reward_be) + ',' +
str(total_reward_af) + '\n')
log_reward_dist_file.write(
str(batch) + ',' + str(dist_reward_be) + ',' +
str(dist_reward_af) + '\n')
log_reward_col_file.write(
str(batch) + ',' + str(col_reward_be) + ',' + str(col_reward_af) +
'\n')
log_reward_total_file.close(
) # not sure if open and close immediantly will help save the appended logs in-place
log_reward_dist_file.close()
log_reward_col_file.close()
writer.add_scalar('total_rewards/before_update', total_reward_be,
batch)
writer.add_scalar('total_rewards/after_update', total_reward_af, batch)
writer.add_scalar('distance_reward/before_update', dist_reward_be,
batch)
writer.add_scalar('distance_reward/after_update', dist_reward_af,
batch)
writer.add_scalar('collison_rewards/before_update', col_reward_be,
batch)
writer.add_scalar('collison_rewards/after_update', col_reward_af,
batch)
if batch % args.save_every == 0: # maybe it can save time/space if the models are saved only periodically
# Save policy network
print('Saving model {}'.format(batch))
with open(os.path.join(save_folder, 'policy-{0}.pt'.format(batch)),
'wb') as f:
torch.save(policy.state_dict(), f)
if batch % 30 == 0:
with open(
os.path.join(
log_traj_folder,
'train_episodes_observ_' + str(batch) + '.pkl'),
'wb') as f:
pickle.dump(
[ep.observations.cpu().numpy() for ep, _ in episodes], f)
with open(
os.path.join(
log_traj_folder,
'valid_episodes_observ_' + str(batch) + '.pkl'),
'wb') as f:
pickle.dump(
[ep.observations.cpu().numpy() for _, ep in episodes], f)
# with open(os.path.join(log_traj_folder, 'train_episodes_ped_state_'+str(batch)+'.pkl'), 'wb') as f:
# pickle.dump([ep.hid_observations.cpu().numpy() for ep, _ in episodes], f)
# with open(os.path.join(log_traj_folder, 'valid_episodes_ped_state_'+str(batch)+'.pkl'), 'wb') as f:
# pickle.dump([ep.hid_observations.cpu().numpy() for _, ep in episodes], f)
# save tasks
# a sample task list of 2: [{'goal': array([0.0209588 , 0.15981938])}, {'goal': array([0.45034602, 0.17282322])}]
with open(
os.path.join(log_traj_folder,
'tasks_' + str(batch) + '.pkl'), 'wb') as f:
pickle.dump(tasks, f)
else:
# supposed to be overwritten for each batch
with open(
os.path.join(log_traj_folder,
'latest_train_episodes_observ.pkl'),
'wb') as f:
pickle.dump(
[ep.observations.cpu().numpy() for ep, _ in episodes], f)
with open(
os.path.join(log_traj_folder,
'latest_valid_episodes_observ.pkl'),
'wb') as f:
pickle.dump(
[ep.observations.cpu().numpy() for _, ep in episodes], f)
# with open(os.path.join(log_traj_folder, 'latest_train_episodes_ped_state.pkl'), 'wb') as f:
# pickle.dump([ep.hid_observations.cpu().numpy() for ep, _ in episodes], f)
# with open(os.path.join(log_traj_folder, 'latest_valid_episodes_ped_state.pkl'), 'wb') as f:
# pickle.dump([ep.hid_observations.cpu().numpy() for _, ep in episodes], f)
with open(os.path.join(log_traj_folder, 'latest_tasks.pkl'),
'wb') as f:
pickle.dump(tasks, f)
print('finished epoch {}; time elapsed: {}'.format(
batch, time_elapsed(time.time() - start_time)))
def main(args):
wandb.config.update({
k: v
for k, v in vars(args).items()
if k in ['env_name', 'tau', 'critic_lr']
})
continuous_actions = (args.env_name in [
'AntVel-v1', 'AntDir-v1', 'AntPos-v0', 'HalfCheetahVel-v1',
'HalfCheetahDir-v1', '2DNavigation-v0'
])
save_folder = './saves/{0}'.format(args.output_folder)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
with open(os.path.join(save_folder, 'config.json'), 'w') as f:
config = {k: v for (k, v) in vars(args).items() if k != 'device'}
config.update(device=args.device.type)
json.dump(config, f, indent=2)
sampler = BatchSampler(args.env_name,
args.seed,
batch_size=args.fast_batch_size,
num_workers=args.num_workers)
if continuous_actions:
policy = NormalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
int(np.prod(sampler.envs.action_space.shape)),
hidden_sizes=(args.hidden_size, ) * args.num_layers)
else:
policy = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size, ) * args.num_layers)
baseline = LinearFeatureBaseline(
int(np.prod(sampler.envs.observation_space.shape)))
critic = Critic(int(np.prod(sampler.envs.observation_space.shape)),
1,
hidden_sizes=(args.hidden_size, ) * args.num_layers)
metalearner = ActorCriticMetaLearner(sampler,
policy,
critic,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device,
critic_lr=args.critic_lr)
wandb.watch(metalearner.critic)
for batch in range(args.num_batches):
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
episodes = metalearner.sample(tasks, first_order=args.first_order)
meta_critic_loss = metalearner.step(
episodes,
max_kl=args.max_kl,
cg_iters=args.cg_iters,
cg_damping=args.cg_damping,
ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
# Logging
wandb.log(
{
'total_rewards/before_update':
total_rewards([ep.rewards for ep, _ in episodes])
},
step=batch)
wandb.log(
{
'total_rewards/after_update':
total_rewards([ep.rewards for _, ep in episodes])
},
step=batch)
wandb.log({'meta critic loss': meta_critic_loss.detach().item()},
step=batch)
# Save policy network
with open(os.path.join(save_folder, 'policy-{0}.pt'.format(batch)),
'wb') as f:
torch.save(policy.state_dict(), f)
def main(args):
continuous_actions = (args.env_name in ['AntVel-v1', 'AntDir-v1',
'AntPos-v0', 'HalfCheetahVel-v1', 'HalfCheetahDir-v1',
'2DNavigation-v0', '2DPointEnvCorner-v0'])
save_folder = './saves/{0}'.format(args.env_name+'/'+args.output_folder)
if args.output_folder!='maml-trial' and args.output_folder!='trial':
i=0
while os.path.exists(save_folder):
args.output_folder=str(i+1)
i+=1
save_folder = './saves/{0}'.format(args.env_name+'/'+args.output_folder)
log_directory = './logs/{0}'.format(args.env_name+'/'+args.output_folder)
os.makedirs(save_folder)
writer = SummaryWriter('./logs/{0}'.format(args.env_name+'/'+args.output_folder))
with open(os.path.join(save_folder, 'config.json'), 'w') as f:
config = {k: v for (k, v) in vars(args).items() if k != 'device'}
config.update(device=args.device.type)
json.dump(config, f, indent=2)
sampler = BatchSampler(args.env_name, batch_size=args.fast_batch_size,
num_workers=args.num_workers)
if continuous_actions:
policy = NormalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
int(np.prod(sampler.envs.action_space.shape)),
hidden_sizes=(args.hidden_size,) * args.num_layers)
else:
policy = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size,) * args.num_layers)
baseline = LinearFeatureBaseline(
int(np.prod(sampler.envs.observation_space.shape)))
if args.load_dir is not None:
policy.load_state_dict(torch.load(args.load_dir))
metalearner = MetaLearner(sampler, policy, baseline, args, gamma=args.gamma,
fast_lr=args.fast_lr, tau=args.tau, device=args.device)
for batch in range(args.num_batches):
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
episodes = metalearner.sample(tasks, first_order=args.first_order)
metalearner.step(episodes, max_kl=args.max_kl, cg_iters=args.cg_iters,
cg_damping=args.cg_damping, ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
print('total_rewards/before_update', total_rewards([ep.rewards for ep, _ in episodes]), batch)
print('total_rewards/after_update', total_rewards([ep.rewards for _, ep in episodes]), batch)
# Plotting figure
# plotting(episodes, batch, save_folder,args.num_plots)
if args.load_dir is not None:
sys.exit(0)
# Tensorboard
writer.add_scalar('total_rewards/before_update',
total_rewards([ep.rewards for ep, _ in episodes]), batch)
writer.add_scalar('total_rewards/after_update',
total_rewards([ep.rewards for _, ep in episodes]), batch)
# Save policy network
with open(os.path.join(save_folder,
'policy-{0}.pt'.format(batch)), 'wb') as f:
torch.save(policy.state_dict(), f)
def main(args):
set_random_seed(args.random)
continuous_actions = (args.env_name in [
'AntVel-v1', 'AntDir-v1', 'AntPos-v0', 'HalfCheetahVel-v1',
'HalfCheetahDir-v1', '2DNavigation-v0', '2DNavigationBiased-v0'
])
writer = SummaryWriter('./logs/{0}'.format(args.alg))
save_folder = './saves/{0}'.format(args.alg)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
with open(os.path.join(save_folder, 'config.json'), 'w') as f:
config = {k: v for (k, v) in vars(args).items() if k != 'device'}
config.update(device=args.device.type)
json.dump(config, f, indent=2)
sampler = BatchSampler(args.env_name,
batch_size=args.fast_batch_size,
num_workers=args.num_workers,
seed=args.random)
if continuous_actions:
policy = NormalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
int(np.prod(sampler.envs.action_space.shape)),
hidden_sizes=(args.hidden_size, ) * args.num_layers)
else:
policy = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size, ) * args.num_layers)
baseline = LinearFeatureBaseline(
int(np.prod(sampler.envs.observation_space.shape)))
if args.alg == 'simul':
# vanilla maml
metalearner = MetaLearner(sampler,
policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device)
for batch in range(args.meta_policy_num * args.num_batches):
# first sample tasks under the distribution
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
# get episodes in the form of (train episodes, test episodes after adaption)
episodes = metalearner.sample(tasks, first_order=args.first_order)
metalearner.step(episodes,
max_kl=args.max_kl,
cg_iters=args.cg_iters,
cg_damping=args.cg_damping,
ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
# Tensorboard
writer.add_scalar(
'maml/before_update',
total_rewards([ep.rewards for ep, _ in episodes]), batch)
writer.add_scalar(
'maml/after_update',
total_rewards([ep.rewards for _, ep in episodes]), batch)
# Save policy network
with open(os.path.join(save_folder, 'policy-{0}.pt'.format(batch)),
'wb') as f:
torch.save(policy.state_dict(), f)
elif args.alg == 'greedy':
# multi-policy maml
metalearner = KPolicyMetaLearner(sampler,
policy,
baseline,
args.meta_policy_num,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device)
# visualize the poolicies' behavior
trajectories = []
for policy_idx in range(args.meta_policy_num):
print(policy_idx)
metalearner.optimize_policy_index(policy_idx)
for batch in range(args.num_batches):
print('batch num %d' % batch)
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
metalearner.evaluate_optimized_policies(tasks)
episodes = metalearner.sample(tasks,
first_order=args.first_order)
# loss is computed inside, then update policies
metalearner.step(episodes,
max_kl=args.max_kl,
cg_iters=args.cg_iters,
cg_damping=args.cg_damping,
ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
# not sure what to write in tensorboard...
for epIdx in range(len(episodes)):
writer.add_scalar(
'kmaml/pi_' + str(policy_idx) + '_task_' + str(epIdx),
total_rewards([episodes[epIdx][1].rewards]), batch)
# use a random task (no update here anyway) to visualize meta-policies
tasks = sampler.sample_tasks(num_tasks=1)
trajectories.append(metalearner.sample_meta_policy(tasks[0]))
plotTrajectories(trajectories)
def main(args):
save_folder = f'saves/{args.output_folder + get_date_str()}'
if not os.path.exists(save_folder):
os.makedirs(save_folder)
with open(os.path.join(save_folder, 'config.json'), 'w') as f:
config = {k: v for (k, v) in vars(args).items() if k != 'device'}
config.update(device=args.device.type)
json.dump(config, f, indent=2)
print('Initializing samplers...')
sampler = BatchSampler(args.env_name,
batch_size=args.fast_batch_size,
num_workers=args.num_workers)
test_sampler = BatchSampler(args.env_name,
test_env=True,
batch_size=args.fast_batch_size,
num_workers=max(1, args.num_workers // 2))
policy = NormalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
int(np.prod(sampler.envs.action_space.shape)),
hidden_sizes=(args.hidden_size, ) * args.num_layers)
baseline = LinearFeatureBaseline(
int(np.prod(sampler.envs.observation_space.shape)))
print('Initializing meta-learners...')
metalearner = MetaLearner(sampler,
policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device) # noqa: E128
# NOTE: we need this metalearner only to sample test tasks
test_metalearner = MetaLearner(test_sampler,
policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device) # noqa: E128
print('Starting the training')
# Initialize logging
wandb.init()
wandb.config.update(args)
task_name2id = {name: i for i, name in enumerate(sampler._env._task_names)}
task_id2name = sampler._env._task_names
task2prob = np.ones(sampler._env.num_tasks) / sampler._env.num_tasks
uniform = np.ones_like(task2prob) / sampler._env.num_tasks
# outer loop (meta-training)
for i in range(args.num_batches):
print(f'Batch {i}')
# sample trajectories from random tasks
print(f'\tSampling a batch of {args.meta_batch_size} training tasks')
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size,
task2prob=0.99 * task2prob +
0.01 * uniform)
# Note: Dirty hack to overcome metaworld dirty hack
task_names = [sampler._env._task_names[t['task']] for t in tasks]
# inner loop (adaptation)
# returns list of tuples (train_episodes, valid_episodes)
print(f'\tTraining')
episodes = metalearner.sample(tasks, first_order=args.first_order)
print(f'\tUpdating the meta-model')
metalearner.step(episodes,
max_kl=args.max_kl,
cg_iters=args.cg_iters,
cg_damping=args.cg_damping,
ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
# Logging
# before: before parameters update
# after: after parameters adaptation to the task
r_before = total_rewards([ep.rewards for ep, _ in episodes])
r_after = total_rewards([ep.rewards for _, ep in episodes])
test_episode_infos = [ep._info_list for ep, _ in episodes]
success_rate_before, task_success_rate_before = get_success_rate(
test_episode_infos, task_names, per_task=True)
test_episode_infos = [ep._info_list for _, ep in episodes]
success_rate_after, task_success_rate_after = get_success_rate(
test_episode_infos, task_names, per_task=True)
wandb.log(
{
'total_rewards/before_update':
r_before,
'total_rewards/after_update':
r_after,
'success_rate/before_update':
success_rate_before,
'success_rate/after_update':
success_rate_after,
'success_rate/improvement':
success_rate_after - success_rate_before,
'success_rate/before_update_macro':
np.mean(list(task_success_rate_before.values())),
'success_rate/after_update_macro':
np.mean(list(task_success_rate_after.values())),
},
step=i)
wandb.log(
{
f'success_rate/after_update/{task}': rate
for task, rate in task_success_rate_after.items()
},
step=i)
wandb.log(
{
f'success_rate/before_update/{task}': rate
for task, rate in task_success_rate_before.items()
},
step=i)
wandb.log(
{
f'success_rate/imrovement/{task}':
task_success_rate_after[task] - task_success_rate_before[task]
for task in task_success_rate_before.keys()
},
step=i)
wandb.log(
{
f'n_acquired_tasks/before_update/at_{x}': sum(
rate > x for rate in task_success_rate_before.values())
for x in [0.001, 0.01, 0.05, 0.1, 0.5]
},
step=i)
wandb.log(
{
f'n_acquired_tasks/after_update/at_{x}': sum(
rate > x for rate in task_success_rate_after.values())
for x in [0.001, 0.01, 0.05, 0.1, 0.5]
},
step=i)
if args.active_learning:
new_task2prob = np.zeros_like(task2prob)
if args.prob_f == 'linear':
norm = 1e-7 + sum(task_success_rate_after.values())
for task, rate in task_success_rate_after.items():
task_id = task_name2id[task]
new_task2prob[task_id] = 1. - rate / norm
elif args.prob_f == 'softmax': # softmax(1 - rate)
# numerical stability trick
# http://cs231n.github.io/linear-classify/#softmax
max_f = 1 - min(task_success_rate_after.values())
for task, rate in task_success_rate_after.items():
task_id = task_name2id[task]
f = 1 - rate
new_task2prob[task_id] = np.exp(
(f - max_f) / args.temperature)
new_task2prob = new_task2prob / (1e-7 + sum(new_task2prob))
elif args.prob_f == 'softmax2': # 1 - softmax(rate)
max_f = max(task_success_rate_after.values())
for task, rate in task_success_rate_after.items():
task_id = task_name2id[task]
new_task2prob[task_id] = np.exp(
(rate - max_f) / args.temperature)
new_task2prob = 1. - new_task2prob / (1e-7 +
sum(new_task2prob))
else:
raise RuntimeError(
'prob-f should be either "softmax", "softmax2" or "linear"'
)
alpha = args.success_rate_smoothing
task2prob = alpha * task2prob + (1 - alpha) * new_task2prob
task2prob /= sum(task2prob)
assert all(task2prob > 0) # strictly!
wandb.log(
{
f'task2prob/{task_id2name[task_id]}': prob
for task_id, prob in enumerate(task2prob)
},
step=i)
# meta-test
if i % args.eval_every == 0:
print(f'Evaluating on meta-test')
# save policy network
_save_path = os.path.join(save_folder, 'policy-{0}.pt'.format(i))
with open(_save_path, 'wb') as f:
torch.save(policy.state_dict(), f)
wandb.save(_save_path)
# Evaluate on meta-test
tasks = test_sampler.sample_tasks(num_tasks=2 *
args.meta_batch_size)
# Note: Dirty hack to overcome metaworld dirty hack
task_names = [
test_sampler._env._task_names[t['task']] for t in tasks
]
episodes = test_metalearner.sample(tasks,
first_order=args.first_order)
r_before = total_rewards([ep.rewards for ep, _ in episodes])
r_after = total_rewards([ep.rewards for _, ep in episodes])
test_episode_infos = [ep._info_list for ep, _ in episodes]
success_rate_before, task_success_rate_before = get_success_rate(
test_episode_infos, task_names, per_task=True)
test_episode_infos = [ep._info_list for _, ep in episodes]
success_rate_after, task_success_rate_after = get_success_rate(
test_episode_infos, task_names, per_task=True)
wandb.log(
{
'total_rewards_test/before_update':
r_before,
'total_rewards_test/after_update':
r_after,
'success_rate_test/before_update':
success_rate_before,
'success_rate_test/after_update':
success_rate_after,
'success_rate_test/improvement':
success_rate_after - success_rate_before
},
step=i)
wandb.log(
{
f'success_rate_test/after_update/{task}': rate
for task, rate in task_success_rate_after.items()
}, # noqa: E501
step=i)
wandb.log(
{
f'success_rate_test/before_update/{task}': rate
for task, rate in task_success_rate_before.items()
}, # noqa: E501
step=i)
wandb.log(
{
f'success_rate_test/imrovement/{task}':
task_success_rate_after[task] -
task_success_rate_before[task]
for task in task_success_rate_before.keys()
},
step=i)
print('Saving the final model')
# save final policy
_save_path = os.path.join(save_folder, 'policy-final.pt')
with open(_save_path, 'wb') as f:
torch.save(policy.state_dict(), f)
wandb.save(_save_path)
def main(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
torch.manual_seed(args.seed)
continuous_actions = (args.env_name in [
'AntVel-v1', 'AntDir-v1', 'AntPos-v0', 'HalfCheetahVel-v1',
'HalfCheetahDir-v1', '2DNavigation-v0'
])
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_folder = './saves/{0}'.format(args.output_folder)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
with open(os.path.join(save_folder, 'config.json'), 'w') as f:
config = {k: v for (k, v) in vars(args).items() if k != 'device'}
config.update(device=args.device.type)
json.dump(config, f, indent=2)
sampler = BatchSampler(args.env_name,
batch_size=args.fast_batch_size,
num_workers=args.num_workers)
if continuous_actions:
policy = NormalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
int(np.prod(sampler.envs.action_space.shape)),
hidden_sizes=(args.hidden_size, ) * args.num_layers)
else:
policy = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size, ) * args.num_layers)
baseline = LinearFeatureBaseline(
int(np.prod(sampler.envs.observation_space.shape)))
metalearner = MetaLearner(
sampler,
policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
q_inner=args.inner_q == 'true',
q_residuce_gradient=args.inner_q_residue_gradient == 'true',
q_soft=args.inner_q_soft == 'true',
q_soft_temp=args.inner_q_soft_temp,
device=args.device,
)
for batch in range(args.num_batches):
if args.device.type == 'cuda':
torch.cuda.empty_cache()
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
episodes, adaptation_info = metalearner.sample(
tasks, first_order=args.first_order)
metalearner.step(episodes,
max_kl=args.max_kl,
cg_iters=args.cg_iters,
cg_damping=args.cg_damping,
ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
# Tensorboard
pre_update_rewards = total_rewards([ep.rewards for ep, _ in episodes])
post_update_rewards = total_rewards([ep.rewards for _, ep in episodes])
writer.add_scalar('total_rewards/before_update', pre_update_rewards,
batch)
writer.add_scalar('total_rewards/after_update', post_update_rewards,
batch)
writer.add_scalar('total_rewards/rewards_improvement',
post_update_rewards - pre_update_rewards, batch)
writer.add_scalar('adaptation/pre_update_inner_loss',
adaptation_info.mean_pre_update_loss, batch)
writer.add_scalar('adaptation/post_update_inner_loss',
adaptation_info.mean_post_update_loss, batch)
writer.add_scalar('adaptation/inner_loss_improvement',
adaptation_info.mean_loss_improvment, batch)
writer.add_scalar('adaptation/weight_change',
adaptation_info.mean_weight_change, batch)
# Save policy network
with open(os.path.join(save_folder, 'policy-{0}.pt'.format(batch)),
'wb') as f:
torch.save(policy.state_dict(), f)
task_success = []
for _ in range(buckets):
tasks = env.unwrapped.sample_tasks(num_test_tasks)
success = 0
#times = []
metalearner = gradient_step(0, tasks, args)
for task in tasks:
s = env.reset_task(task)
step = 0
d = False
while not d:
#env.render()
input = torch.tensor(s).float()
action = model.forward(
input,
model.state_dict()).rsample().detach().numpy()
step += 1
s, r, d, info = env.step(action)
if r == 1:
success += 1
# times.append(step)
# maml.append(times)
task_success.append(success / num_test_tasks)
successes.append(task_success)
env.close()
#out = [successes, maml]
if not os.path.exists('./pkls'):
os.makedirs('./pkls')
with open('./pkls/{0}.pkl'.format(args.output_folder), 'wb') as f:
pickle.dump(successes, f)