def main(args):
args.output_folder = args.env_name
# TODO
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).iteritems() if k != 'device'}
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(config)
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)), # input shape
int(np.prod(sampler.envs.action_space.shape)), # output shape
hidden_sizes=(args.hidden_size,) * args.num_layers) # [100, 100]
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): # number of epoches
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)
print(batch, total_rewards([ep.rewards for ep, _ in episodes]), total_rewards([ep.rewards for _, ep in episodes]))
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 [
'AntVelEnv-v1', 'AntDirEnv-v1', 'HalfCheetahVelEnv-v1',
'HalfCheetahDirEnv-v1', '2DNavigation-v0'
])
save_folder = os.path.join('tmp', args.output_folder)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
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)))
# Load model
with open(args.model, 'rb') as f:
state_dict = torch.load(f)
policy.load_state_dict(state_dict)
metalearner = MetaLearner(sampler,
policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device)
args.meta_batch_size = 81
# velocities = np.linspace(-1., 3., num=args.meta_batch_size)
# tasks = [{'velocity': velocity} for velocity in velocities]
tasks = [{'direction': direction} for direction in [-1, 1]]
for batch in range(args.num_batches):
episodes = metalearner.sample(tasks)
train_returns = [ep.rewards.sum(0).cpu().numpy() for ep, _ in episodes]
valid_returns = [ep.rewards.sum(0).cpu().numpy() for _, ep in episodes]
with open(os.path.join(save_folder, '{0}.npz'.format(batch)),
'wb') as f:
np.savez(f, train=train_returns, valid=valid_returns)
print('Batch {0}'.format(batch))
def main(args):
np.random.seed(RANDOM_SEED)
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 = MrcBatchSampler(args.env_name, batch_size=args.fast_batch_size, train_folder=TRAIN_TRACES)
policy = ActorNet(input_size=[S_INFO, S_LEN], output_size=A_DIM)
baseline = CriticNet(input_size=[S_INFO, S_LEN], output_size=A_DIM)
# baseline.load_state_dict(torch.load(os.path.join(save_folder, 'baseline-2000.pt')))
# policy.load_state_dict(torch.load(os.path.join(save_folder, 'policy-2000.pt')))
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()
print("===================================================================")
print("=====================Now epoch: ", 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)
print("total_rewards/before_update ", total_rewards([ep.rewards for ep, _ in episodes]))
print('total_rewards/after_update', total_rewards([ep.rewards for _, ep in episodes]))
# # 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 not batch % 5:
metaTest(policy, baseline, batch, args.meta_batch_size, args.fast_batch_size)
# Save policy network
with open(os.path.join(save_folder,
'meta-policy-{0}.pt'.format(batch)), 'wb') as f:
torch.save(policy.state_dict(), f)
with open(os.path.join(save_folder,
'meat-baseline-{0}.pt'.format(batch)), 'wb') as f:
torch.save(baseline.state_dict(), f)
def main(args):
env_name = 'RVONavigationAll-v0' #['2DNavigation-v0', 'RVONavigation-v0', 'RVONavigationAll-v0']
test_folder = './{0}'.format('test_nav')
fast_batch_size = 40 # number of trajectories
saved_policy_file = os.path.join(
'./TrainingResults/result3/saves/{0}'.format('maml-2DNavigation-dir'),
'policy-180.pt')
sampler = BatchSampler(env_name, batch_size=fast_batch_size, num_workers=3)
policy = NormalMLPPolicy(int(np.prod(
sampler.envs.observation_space.shape)),
int(np.prod(sampler.envs.action_space.shape)),
hidden_sizes=(100, ) * 2)
# Loading policy
if os.path.isfile(saved_policy_file):
policy_info = torch.load(saved_policy_file,
map_location=lambda storage, loc: storage)
policy.load_state_dict(policy_info)
print('Loaded saved policy')
else:
sys.exit("The requested policy does not exist for loading")
# Creating test folder
if not os.path.exists(test_folder):
os.makedirs(test_folder)
# Generate tasks
# goal = [[-0.8, 0.9]]
# task = [{'goal': goal}][0]
tasks = sampler.sample_tasks(num_tasks=1)
task = tasks[0]
# Start validation
print("Starting to test...Total step = ", args.grad_steps)
start_time = time.time()
# baseline = LinearFeatureBaseline(int(np.prod(sampler.envs.observation_space.shape)))
baseline = LinearFeatureBaseline(int(np.prod((2, ))))
metalearner = MetaLearner(sampler,
policy,
baseline,
gamma=0.9,
fast_lr=0.01,
tau=0.99,
device='cpu')
# test_episodes = metalearner.sample(tasks)
# for train, valid in test_episodes:
# total_reward, dist_reward, col_reward = total_rewards(train.rewards)
# print(total_reward)
# total_reward, dist_reward, col_reward = total_rewards(valid.rewards)
# print(total_reward)
test_episodes = metalearner.test(task, n_grad=args.grad_steps)
print('-------------------')
for n_grad, ep in test_episodes:
total_reward, dist_reward, col_reward = total_rewards(ep.rewards)
print(total_reward)
# with open(os.path.join(test_folder, 'test_episodes_grad'+str(n_grad)+'.pkl'), 'wb') as f:
# pickle.dump([ep.observations.cpu().numpy(), ep], f)
# with open(os.path.join(test_folder, 'task.pkl'), 'wb') as f:
# pickle.dump(task, f)
print('Finished test. Time elapsed = {}'.format(
time_elapsed(time.time() - start_time)))
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 run_meta_training(self, final_model_folder, policy=None):
parser = argparse.ArgumentParser(
description='Reinforcement learning with '
'Model-Agnostic Meta-Learning (MAML)')
# General
parser.add_argument('--env-name',
type=str,
help='name of the environment',
default='BiddingMDP-v0')
parser.add_argument('--gamma',
type=float,
default=0.95,
help='value of the discount factor gamma')
parser.add_argument('--tau',
type=float,
default=1.0,
help='value of the discount factor for GAE')
parser.add_argument('--first-order',
action='store_true',
help='use the first-order approximation of MAML')
# Policy network (relu activation function)
#parser.add_argument('--hidden-size', type=int, default=50,
parser.add_argument('--hidden-size',
type=int,
default=200,
help='number of hidden units per layer')
parser.add_argument('--num-layers',
type=int,
default=2,
help='number of hidden layers')
# Task-specific
parser.add_argument('--fast-batch-size',
type=int,
default=20,
help='batch size for each individual task')
parser.add_argument(
'--fast-lr',
type=float,
default=0.5,
help='learning rate for the 1-step gradient update of MAML')
# Optimization
parser.add_argument(
'--num-batches',
type=int,
default=32,
# parser.add_argument('--num-batches', type=int, default=32,
# parser.add_argument('--num-batches', type=int, default=50,
help='number of batches')
#parser.add_argument('--meta-batch-size', type=int, default=50,
# parser.add_argument('--meta-batch-size', type=int, default=50,
parser.add_argument('--meta-batch-size',
type=int,
default=2,
help='number of tasks per batch')
parser.add_argument('--max-kl',
type=float,
default=1e-2,
help='maximum value for the KL constraint in TRPO')
parser.add_argument('--cg-iters',
type=int,
default=10,
help='number of iterations of conjugate gradient')
parser.add_argument('--cg-damping',
type=float,
default=1e-5,
help='damping in conjugate gradient')
# parser.add_argument('--ls-max-steps', type=int, default=2,
parser.add_argument(
'--ls-max-steps',
type=int,
default=15,
# parser.add_argument('--ls-max-steps', type=int, default=15,
help='maximum number of iterations for line search')
parser.add_argument(
'--ls-backtrack-ratio',
type=float,
default=0.8,
help='maximum number of iterations for line search')
# Miscellaneous
parser.add_argument('--output-folder',
type=str,
default='maml',
help='name of the output folder')
# parser.add_argument('--num-workers', type=int, default=mp.cpu_count() - 2,
parser.add_argument('--num-workers',
type=int,
default=4,
help='number of workers for trajectories sampling')
parser.add_argument('--device',
type=str,
default='cuda',
help='set the device (cpu or cuda)')
args = parser.parse_args()
self.fast_batch_size = args.fast_batch_size
self.max_kl = args.max_kl
self.cg_iters = args.cg_iters
self.first_order = args.first_order
self.cg_damping = args.cg_damping
self.ls_max_steps = args.ls_max_steps
self.ls_backtrack_ratio = args.ls_backtrack_ratio
self.output_folder = args.output_folder
self.num_batches = args.num_batches
continuous_actions = (args.env_name in [
'BiddingMDP-v0', 'AntVel-v1', 'AntDir-v1', 'AntPos-v0',
'HalfCheetahVel-v1', 'HalfCheetahDir-v1', '2DNavigation-v0'
])
# Create logs and saves folder if they don't exist
if not os.path.exists('./logs'):
os.makedirs('./logs')
if not os.path.exists('./saves'):
os.makedirs('./saves')
# Device
# args.device = torch.device(args.device
# if torch.cuda.is_available() else 'cpu')
args.device = torch.device("cpu")
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 policy is None and continuous_actions:
print("CREATING POLICY WHEN IT SHOULD NOT")
exit()
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)
self.policy = policy
elif policy is None:
print("CREATING POLICY WHEN IT SHOULD NOT")
exit()
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)))
self.policy = policy
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):
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)
torch.cuda.empty_cache()
# Save policy network
final_model_path = final_model_folder + "meta_rl_gamma_policy_{}.pt".format(
batch)
with open(final_model_path, 'wb') as f:
torch.save(policy.state_dict(), f)
self.metalearner = metalearner
return final_model_path
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)))
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size, ) * args.num_layers)
#loading the model
save_folder = './saves/{0}'.format(args.output_folder)
the_model.load_state_dict(
torch.load(os.path.join(save_folder, 'policy-{0}.pt'.format(batch))))
baseline = LinearFeatureBaseline(
int(np.prod(sampler.envs.observation_space.shape)))
metalearner = MetaLearner(sampler,
the_model,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device)
env = gym.make(args.env_name)
# new task!
episodes = []
#randomly sample task
test_task = sampler.sample_tasks(num_tasks=1)
#set specific task.
#test_task = []
#test_task.append({'velocity': 1.9})
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)
def main(args):
continuous_actions = (args.env_name in [
'AntVel-v1', 'AntDir-v1', 'AntPos-v0', 'HalfCheetahVel-v1',
'HalfCheetahDir-v1', '2DNavigation-v0', 'Pusher'
])
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)
if not args.hierarchical:
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 i, batch in enumerate(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',
str(total_rewards([ep.rewards for _, ep in episodes])))
# 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 (i + 1) % args.save_every == 0:
# Save policy network
with open(
os.path.join(save_folder,
'policy-{0}.pt'.format(batch)),
'wb') as f:
torch.save(policy, f)
else:
sampler = BatchSampler(args.env_name,
batch_size=args.fast_batch_size,
num_workers=args.num_workers)
# Get the policies
higher_policy, lower_trainer, baseline = hierarchical_meta_policy(
args.env_name,
args.skills_dim,
sampler=sampler,
net_size=args.hidden_size,
output_size=1)
# Define the hierarchical meta learner
hr_meta_learner = HierarchicalMetaLearner(sampler,
higher_policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device)
# Training procedure
for i, batch in enumerate(range(args.num_batches)):
# Train the lower level policy
lower_trainer.train()
# Now freeze the lower level policy
lower_networks = lower_trainer.networks
lower_policy = lower_networks[0]
lower_policy.trainable = False
# Sample the different tasks
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
# Sample the different episodes for the different tasks
episodes = hr_meta_learner.sample(tasks,
lower_policy,
first_order=args.first_order)
hr_meta_learner.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',
str(total_rewards([ep.rewards for _, ep in episodes])))
lower_policy.trainable = True
# 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 (i + 1) % args.save_every == 0:
# Save the policy networks
with open(
os.path.join(save_folder,
'h_policy-{0}.pt'.format(batch)),
'wb') as f:
torch.save(higher_policy, f)
with open(
os.path.join(save_folder,
'l_policy-{0}.pt'.format(batch)),
'wb') as f:
torch.save(lower_policy, f)
with open(os.path.join(save_folder, 'baseline.pt'), 'wb') as f:
torch.save(baseline, f)
def train_pretrained_model(args):
continuous_actions = (args.env_name in [
'AntVel-v1', 'AntDir-v1', 'AntPos-v0', 'HalfCheetahVel-v1',
'HalfCheetahDir-v1', '2DNavigation-v0', 'MountainCarContinuousVT-v0'
])
# writer = SummaryWriter('./logs/{0}'.format(args.output_folder + '_pretrained'))
save_folder = './saves/{0}'.format(args.output_folder + '_pretrained')
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)
#batch_size=2*args.fast_batch_size to match the amount of data used in meta-learning
sampler = BatchSampler(args.env_name,
batch_size=2 * 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)))
#load pretrained model
cont_from_batch = 0
if args.start_from_batch != -1:
pretrained_model = os.path.join(
save_folder, 'policy-{0}.pt'.format(args.start_from_batch - 1))
if os.path.exists(pretrained_model):
policy.load_state_dict(torch.load(pretrained_model))
cont_from_batch = args.start_from_batch
metalearner = MetaLearner(sampler,
policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device)
for batch in range(cont_from_batch, args.num_batches):
print('Currently processing Batch: {}'.format(batch + 1))
task_sampling_time = time.time()
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
task_sampling_time = time.time() - task_sampling_time
episode_generating_time = time.time()
episodes = metalearner.sample_for_pretraining(
tasks, first_order=args.first_order)
episode_generating_time = time.time() - episode_generating_time
learning_step_time = time.time()
params = metalearner.adapt(episodes, first_order=args.first_order)
metalearner.policy.load_state_dict(params, strict=True)
learning_step_time = time.time() - learning_step_time
print('Tasking Sampling Time: {}'.format(task_sampling_time))
print('Episode Generating Time: {}'.format(episode_generating_time))
print('Learning Step Time: {}'.format(learning_step_time))
# 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)
# experiment.log_metric("Avg Disc Reward (Pretrained)", total_rewards([episodes.rewards], args.gamma), batch+1)
# Save policy network
with open(os.path.join(save_folder, 'policy-{0}.pt'.format(batch)),
'wb') as f:
torch.save(metalearner.policy.state_dict(), f)
return
def main(args):
# Setup for logging
tb_writer = SummaryWriter('./logs/tb_{}'.format(
args.log_name)) # Tensorboard logging
log = set_log(args)
# Setup before meta-train starts
sampler = BatchSampler(env_name=args.env_name,
batch_size=args.fast_batch_size,
num_workers=args.num_workers,
args=args)
# NOTE Observation space is a list with [predator0, predator1, ..., prey]
# Thus using the index of 0
policy = NormalMLPPolicy(
input_size=int(np.prod(sampler.envs.observation_space[0].shape)),
output_size=int(np.prod(sampler.envs.action_space[0].shape)),
hidden_sizes=(args.hidden_size, ) * args.num_layers)
baseline = LinearFeatureBaseline(
input_size=int(np.prod(sampler.envs.observation_space[0].shape)))
meta_learner = MetaLearner(sampler,
policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device,
args=args,
log=log,
tb_writer=tb_writer)
# meta_learner.load(
# filename="theta_200", directory="./pytorch_models")
meta_tester = MetaTester(sampler,
policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device,
args=args,
log=log,
tb_writer=tb_writer)
prey = Prey(env=sampler._env,
args=args,
log=log,
tb_writer=tb_writer,
name="prey",
i_agent=0)
# Meta-train starts
iteration = 0
while True:
# Sample train and validation episode
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size,
test=False)
episodes = meta_learner.sample(tasks,
prey,
first_order=args.first_order,
iteration=iteration)
# Train meta-policy
meta_learner.step(episodes=episodes, args=args)
# Test meta-policy
if iteration % 10 == 0:
test_tasks = sampler.sample_tasks(num_tasks=5, test=True)
meta_tester.few_shot_adaptation(meta_policy=meta_learner.policy,
tasks=test_tasks,
first_order=args.first_order,
iteration=iteration,
prey=prey)
if iteration % 100 == 0:
meta_learner.save(iteration)
iteration += 1
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):
continuous_actions = (args.env_name in [
'AntVel-v1', 'AntDir-v1', 'AntPos-v0', 'HalfCheetahVel-v1',
'HalfCheetahDir-v1', '2DNavigation-v0', 'Point2DWalls-corner-v0',
'Ant-v0', 'HalfCheetah-v0'
])
writer = SummaryWriter(log_dir=args.log_dir)
logger.configure(dir=args.log_dir, format_strs=['stdout', 'log', 'csv'])
logger.log(args)
json.dump(vars(args),
open(os.path.join(
args.log_dir,
'params.json',
), 'w'),
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):
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)
logger.logkv('return_avg_pre',
total_rewards([ep.rewards for ep, _ in episodes]))
logger.logkv('return_avg_post',
total_rewards([ep.rewards for _, ep in episodes]))
logger.dumpkvs()
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):
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 train_meta_learning_model(args):
# import matplotlib.pyplot as plt
# import matplotlib.animation as animation
# from matplotlib import style
# style.use('fivethirtyeight')
# fig = plt.figure()
# ax1 = fig.add_subplot(1,1,1)
# xs = []
# ys = []
# def animate(i):
# ax1.clear()
# ax1.plot(xs, ys)
rewards_before_ml = []
rewards_after_ml = []
continuous_actions = (args.env_name in [
'AntVel-v1', 'AntDir-v1', 'AntPos-v0', 'HalfCheetahVel-v1',
'HalfCheetahDir-v1', '2DNavigation-v0', 'MountainCarContinuousVT-v0'
])
# writer = SummaryWriter('./logs/{0}'.format(args.output_folder + '_metalearned'))
save_folder = './saves/{0}'.format(args.output_folder + '_metalearned')
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)
torch.manual_seed(args.random_seed)
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)))
#load pretrained model
cont_from_batch = 0
if args.start_from_batch != -1:
metalearned_model = os.path.join(
save_folder, 'policy-{0}.pt'.format(args.start_from_batch - 1))
if os.path.exists(metalearned_model):
policy.load_state_dict(torch.load(metalearned_model))
cont_from_batch = args.start_from_batch
metalearner = MetaLearner(sampler,
policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device)
for batch in range(cont_from_batch, args.num_batches):
print('Currently processing Batch: {}'.format(batch + 1))
task_sampling_time = time.time()
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size,
sampling_type=args.sampling_type,
points_per_dim=args.points_per_dim)
task_sampling_time = time.time() - task_sampling_time
episode_generating_time = time.time()
episodes = metalearner.sample(tasks, first_order=args.first_order)
episode_generating_time = time.time() - episode_generating_time
learning_step_time = time.time()
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)
learning_step_time = time.time() - learning_step_time
print('Tasking Sampling Time: {}'.format(task_sampling_time))
print('Episode Generating Time: {}'.format(episode_generating_time))
print('Learning Step Time: {}'.format(learning_step_time))
reward_before_ml = total_rewards([ep.rewards for ep, _ in episodes],
args.gamma)
reward_after_ml = total_rewards([ep.rewards for _, ep in episodes],
args.gamma)
print('Before Update: {} After Update: {}'.format(
reward_before_ml, reward_after_ml))
# experiment.log_metric("Avg Reward Before Update (MetaLearned)", reward_before_ml)
experiment.log_metric("Avg Reward", reward_after_ml, batch + 1)
rewards_before_ml.append(reward_before_ml)
rewards_after_ml.append(reward_after_ml)
# xs.append(batch+1)
# ys.append(total_rewards([ep.rewards for _, ep in episodes], args.gamma))
# ani = animation.FuncAnimation(fig, animate, interval=1000)
# plt.savefig('navg_baseline_monitor')
# Save policy network
with open(os.path.join(save_folder, 'policy-{0}.pt'.format(batch)),
'wb') as f:
torch.save(metalearner.policy.state_dict(), f)
# tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
# episodes = metalearner.sample(tasks, first_order=args.first_order)
# print("Avg Reward After Update (MetaLearned)", total_rewards([ep.rewards for _, ep in episodes], args.gamma))
testing_sampler = BatchSampler(args.env_name,
batch_size=args.testing_fbs,
num_workers=args.num_workers)
testing_metalearner = MetaLearner(testing_sampler,
metalearner.policy,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device)
test_tasks = testing_sampler.sample_tasks(num_tasks=args.testing_mbs,
sampling_type='rand',
points_per_dim=-1)
test_episodes = testing_metalearner.sample(test_tasks,
first_order=args.first_order,
no_update=True)
test_reward = total_rewards([ep.rewards for ep in test_episodes],
args.gamma)
print('-------------------------------------------------')
print('Test Time reward is: ' + str(test_reward))
print('-------------------------------------------------')
pickle_reward_data_file = os.path.join(save_folder, 'reward_data.pkl')
with open(pickle_reward_data_file, 'wb') as f:
pickle.dump(rewards_before_ml, f)
pickle.dump(rewards_after_ml, f)
pickle_final_reward_file = os.path.join(save_folder, 'final_reward.pkl')
with open(pickle_final_reward_file, 'wb') as f:
pickle.dump(test_reward, f)
return
def main(args):
wandb.config.update(
{k: v
for k, v in vars(args).items() if k in ['env_name', 'tau']})
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 = 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):
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)
# 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)
# 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', 'Point2DWalls-corner-v0',
'Ant-v0', 'HalfCheetah-v0'
])
logger.configure(dir=args.log_dir, format_strs=['stdout', 'log', 'csv'])
logger.log(args)
json.dump(vars(args),
open(os.path.join(
args.log_dir,
'params.json',
), 'w'),
indent=2)
sampler = BatchSamplerMultiworld(args)
sampler_val = BatchSamplerMultiworld(args, val=True)
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,
bias_transformation_size=args.bias_transformation_size,
init_gain=args.init_gain,
)
else:
raise NotImplementedError
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,
entropy_coef=args.entropy_coef,
device=args.device)
start_time = time.time()
processes = []
for batch in range(args.num_batches):
metalearner.reset()
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
episodes = metalearner.sample(tasks, first_order=args.first_order)
if sampler.rewarder.fit_counter > 0:
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)
if batch % args.rewarder_fit_period == 0:
sampler.fit_rewarder(logger)
if args.rewarder == 'unsupervised':
sampler.log_unsupervised(logger)
log_main(logger, episodes, batch, args, start_time, metalearner)
if batch % args.save_period == 0 or batch == args.num_batches - 1:
save_model_maml(args, policy, batch)
if batch % args.val_period == 0 or batch == args.num_batches - 1:
val(args, sampler_val, policy, baseline, batch)
if batch % args.vis_period == 0 or batch == args.num_batches - 1:
if args.plot:
p = Popen(
'python maml_rl/utils/visualize.py --log-dir {}'.format(
args.log_dir),
shell=True)
processes.append(p)
logger.dumpkvs()
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)))