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):
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 eval(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)
log_folder = './logs/{0}'.format(args.output_folder)
if not os.path.exists(log_folder):
os.makedirs(log_folder)
sampler = BatchSampler(args.env_name,
batch_size=args.fast_batch_size,
num_workers=args.num_workers)
if args.env_name == 'AntPos-v0':
param_bounds = {"x": [-3, 3], "y": [-3, 3]}
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)
else:
policy = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size, ) * args.num_layers)
policy.eval()
tree.eval()
all_tasks = []
# torch.autograd.set_detect_anomaly(True)
reward_list = []
for batch in range(args.num_batches + 1):
print("starting iteration {}".format(batch))
try:
policy.load_state_dict(
torch.load(
os.path.join(save_folder, 'policy-{0}.pt'.format(batch))))
tree = torch.load(
os.path.join(save_folder, 'tree-{0}.pt'.format(batch)))
tree.eval()
except Exception:
with open(
'./logs/{0}/reward_list_eval.pkl'.format(
args.output_folder), 'wb') as pf:
pickle.dump(reward_list, pf)
print(reward_list)
return
# tree.load_state_dict(torch.load(os.path.join(save_folder,
# 'tree-{0}.pt'.format(batch))))
tasks = sampler.sample_tasks(args.meta_batch_size)
all_tasks.append(tasks)
# tasks = np.array(tasks)
# tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
with open('./logs/{0}/task_list_eval.pkl'.format(args.output_folder),
'wb') as pf:
pickle.dump(all_tasks, pf)
print("evaluating...".format(batch))
all_rewards = []
for task in tasks:
print(task["position"])
episodes = sampler.sample(policy, task, tree=tree)
# print("training...".format(batch))
# tr = [ep.rewards for ep in episodes]
# tr = np.mean([torch.mean(torch.sum(rewards, dim=0)).item() for rewards in tr])
all_rewards.append(total_rewards(episodes.rewards))
reward_list.append(np.mean(all_rewards))
with open('./logs/{0}/reward_list_eval.pkl'.format(args.output_folder),
'wb') as pf:
pickle.dump(reward_list, pf)
print(reward_list)
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)))
num_workers=args.num_workers)
if continuous_actions:
the_model = 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:
the_model = CategoricalMLPPolicy(
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!
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 k_shot_experiments(args):
continuous_actions = (args.env_name in [
'AntVel-v1', 'AntDir-v1', 'AntPos-v0', 'HalfCheetahVel-v1',
'HalfCheetahDir-v1', '2DNavigation-v0', 'MountainCarContinuousVT-v0'
])
sampler = BatchSampler(args.env_name,
batch_size=args.fast_batch_size,
num_workers=args.num_workers)
if continuous_actions:
policy_pretrained = 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)
policy_metalearned = 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)
policy_random = 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_pretrained = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size, ) * args.num_layers)
policy_metalearned = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size, ) * args.num_layers)
policy_random = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size, ) * args.num_layers)
# save_folder_pretrained = './saves/{0}'.format(args.output_folder + '_pretrained')
# pretrained_model = os.path.join(save_folder_pretrained, 'policy-{0}.pt'.format(args.num_batches-1))
# policy_pretrained.load_state_dict(torch.load(pretrained_model))
save_folder_metalearned = './saves/{0}'.format(args.output_folder +
'_metalearned')
metalearned_model = os.path.join(
save_folder_metalearned, 'policy-{0}.pt'.format(args.num_batches - 1))
policy_metalearned.load_state_dict(torch.load(metalearned_model))
# metalearned_tester = k_shot_tester(args.K_shot_batch_num, policy_metalearned, args.K_shot_batch_size, args.K_shot_num_tasks, 'MetaLearned', args)
# avg_discounted_returns_metalearned = metalearned_tester.run_k_shot_exp()
# print('Metalearned KSHOT result: ', avg_discounted_returns_metalearned)
# print('Mean: ', torch.mean(avg_discounted_returns_metalearned, 0))
results_folder = './saves/{0}'.format(args.output_folder + '_results')
if not os.path.exists(results_folder):
os.makedirs(results_folder)
kshot_fig_path1 = os.path.join(results_folder, 'kshot_testing')
# kshot_fig_path2 = os.path.join(results_folder, 'ml_pre_diff')
result_data_path = os.path.join(results_folder, 'data_')
metalearned_tester = k_shot_tester(args.K_shot_batch_num,
policy_metalearned,
args.K_shot_batch_size,
args.K_shot_num_tasks, 'MetaLearned',
args)
avg_discounted_returns_metalearned = metalearned_tester.run_k_shot_exp()
# pretrained_tester = k_shot_tester(args.K_shot_batch_num, policy_pretrained, args.K_shot_batch_size, args.K_shot_num_tasks, 'Pretrained', args)
# avg_discounted_returns_pretrained = pretrained_tester.run_k_shot_exp()
# random_tester = k_shot_tester(args.K_shot_batch_num, policy_random, args.K_shot_batch_size, args.K_shot_num_tasks, 'Random', args)
# avg_discounted_returns_random = random_tester.run_k_shot_exp()
plt.figure('K Shot: Testing Curves')
# plt.plot([i for i in range(args.K_shot_batch_num + 1)], avg_discounted_returns_pretrained, color=np.array([0.,0.,1.]), label='Pre-Trained')
# plt.plot([i for i in range(args.K_shot_batch_num + 1)], avg_discounted_returns_metalearned, color=np.array([0.,1.,0.]), label='Meta-Learned')
# plt.plot([i for i in range(args.K_shot_batch_num + 1)], avg_discounted_returns_random, color=np.array([0.,0.,0.]), label='Random')
# plt.errorbar([i for i in range(args.K_shot_batch_num + 1)], torch.mean(avg_discounted_returns_pretrained, 0).tolist(), torch.std(avg_discounted_returns_pretrained, 0).tolist(), color=np.array([0.,0.,1.]), label='Pre-Trained', capsize=5, capthick=2)
plt.errorbar([i for i in range(args.K_shot_batch_num + 1)],
torch.mean(avg_discounted_returns_metalearned, 0).tolist(),
torch.std(avg_discounted_returns_metalearned, 0).tolist(),
color=np.array([0., 1., 0.]),
label='Meta-Learned',
capsize=5,
capthick=2)
# plt.errorbar([i for i in range(args.K_shot_batch_num + 1)], torch.mean(avg_discounted_returns_random, 0).tolist(), torch.std(avg_discounted_returns_random, 0).tolist(), color=np.array([0.,0.,0.]), label='Random', capsize=5, capthick=2)
plt.xlabel('Gradient Descent Iteration Number')
plt.ylabel('Average Discounted Return')
plt.title('K Shot: Testing Curves')
plt.legend(loc='upper left')
plt.savefig(kshot_fig_path1)
# plt.show()
# plt.figure('K Shot: Difference between Metalearned and Pretrained')
# plt.errorbar([i for i in range(args.K_shot_batch_num + 1)], torch.mean(avg_discounted_returns_metalearned-avg_discounted_returns_pretrained, 0).tolist(), torch.std(avg_discounted_returns_metalearned-avg_discounted_returns_pretrained, 0).tolist(), color=np.array([0.,0.,0.]), capsize=5, capthick=2)
# plt.xlabel('Gradient Descent Iteration Number')
# plt.ylabel('Average Discounted Return Difference')
# plt.title('K Shot: Difference between Metalearned and Pretrained')
# plt.savefig(kshot_fig_path2)
# plt.show()
#save torch tensor results to combine with other experiments
# torch.save(avg_discounted_returns_pretrained, result_data_path + 'pretrained')
torch.save(avg_discounted_returns_metalearned,
result_data_path + 'metalearned')
return
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):
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)
indexes = [399]
num_test_tasks = 100
buckets = 1
successes = []
for index in indexes:
sampler = BatchSampler(args.env_name,
batch_size=args.fast_batch_size,
num_workers=args.num_workers)
model = 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)
checkpoint = torch.load(
'../final_models/meta/{0}/policy-{1}.pt'.format(
args.to_pickle, index))
model.load_state_dict(checkpoint)
baseline = LinearFeatureBaseline(
int(np.prod(sampler.envs.observation_space.shape)))
metalearner = MetaLearner(sampler,
model,
baseline,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device)
task_success = []
for _ in range(buckets):
tasks = env.unwrapped.sample_tasks(num_test_tasks)
success = 0