def get_params_for_notebook(path):
PARAMS_FILE = path + 'params.json'
with open(PARAMS_FILE) as json_file:
params = json.load(json_file)
env = 'PlaygroundNavigationRender-v1'
seed = np.random.randint(1e6)
params, rank_seed = config.configure_everything(
rank=0,
seed=seed,
num_cpu=params['experiment_params']['n_cpus'],
env=env,
trial_id=0,
n_epochs=10,
reward_function=params['conditions']['reward_function'],
policy_encoding=params['conditions']['policy_encoding'],
feedback_strategy=params['conditions']['feedback_strategy'],
policy_architecture=params['conditions']['policy_architecture'],
goal_invention=params['conditions']['goal_invention'],
reward_checkpoint=params['conditions']['reward_checkpoint'],
rl_positive_ratio=params['conditions']['rl_positive_ratio'],
p_partner_availability=params['conditions']
['p_social_partner_availability'],
imagination_method=params['conditions']['imagination_method'],
git_commit='',
display=False)
return params
def launch(**kwargs):
# Fork for multi-CPU MPI implementation.
rank = fork(kwargs['num_cpu'])
# Configure everything and log parameters
params, rank_seed = config.configure_everything(rank, **kwargs)
# Define language model
policy_language_model, reward_language_model = config.get_language_models(
params)
# Define the one-hot_encoder (vocabulary of words and max_seq_legnth)
onehot_encoder = config.get_one_hot_encoder(params['train_descriptions'] +
params['test_descriptions'])
# Define the goal sampler for training
goal_sampler = GoalSampler(policy_language_model=policy_language_model,
reward_language_model=reward_language_model,
goal_dim=policy_language_model.goal_dim,
one_hot_encoder=onehot_encoder,
params=params)
# Define reward function
reward_function = config.get_reward_function(goal_sampler=goal_sampler,
params=params)
oracle_reward_function = config.get_oracle_reward_function(
goal_sampler, params)
policy_language_model.set_reward_function(reward_function)
if reward_language_model is not None:
reward_language_model.set_reward_function(reward_function)
# Define the goal sampler for evaluation
eval_goal_sampler = EvalGoalSampler(
policy_language_model=policy_language_model,
one_hot_encoder=onehot_encoder,
params=params)
# Give reward function to goal sampler to track metrics
goal_sampler.store_reward_function(reward_function)
# Define learning algorithm
policy = config.configure_learning_algo(reward_function=reward_function,
goal_sampler=goal_sampler,
params=params)
# Define the social partner
social_partner = SocialPartner(
oracle_reward_function=oracle_reward_function,
goal_sampler=goal_sampler,
**params['social_partner_params'],
params=params)
# Define the data processor
data_processor = DataProcessor(
reward_function=reward_function,
oracle_reward_function=oracle_reward_function,
goal_sampler=goal_sampler,
params=params)
# Define the worker to interact with the environment (training and evaluation)
training_worker = RolloutWorker(make_env=params['make_env'],
policy=policy,
reward_function=reward_function,
params=params,
**params['training_rollout_params'])
training_worker.seed(rank_seed)
evaluation_worker = RolloutWorker(make_env=params['make_env'],
policy=policy,
reward_function=reward_function,
params=params,
**params['evaluation_rollout_params'],
render=False)
evaluation_worker.seed(rank_seed * 10)
stats_logger = StatsLogger(goal_sampler=goal_sampler,
data_processor=data_processor,
training_worker=training_worker,
evaluation_worker=evaluation_worker,
reward_function=reward_function,
policy=policy,
params=params)
train(logdir=params['experiment_params']['logdir'],
policy=policy,
training_worker=training_worker,
goal_sampler=goal_sampler,
eval_goal_sampler=eval_goal_sampler,
evaluation_worker=evaluation_worker,
social_partner=social_partner,
n_epochs=params['experiment_params']['n_epochs'],
n_test_rollouts=params['experiment_params']['n_test_rollouts'],
n_cycles=params['experiment_params']['n_cycles'],
n_batches=params['experiment_params']['n_batches'],
reward_function=reward_function,
stats_logger=stats_logger,
data_processor=data_processor,
params=params)
def main(policy_file, seed, n_test_rollouts, render):
set_global_seeds(seed)
# Load params
with open(PARAMS_FILE) as json_file:
params = json.load(json_file)
if not render:
env = 'PlaygroundNavigation-v1'
else:
env = 'PlaygroundNavigationRender-v1'
params, rank_seed = config.configure_everything(
rank=0,
seed=seed,
num_cpu=params['experiment_params']['n_cpus'],
env=env,
trial_id=0,
n_epochs=10,
reward_function=params['conditions']['reward_function'],
policy_encoding=params['conditions']['policy_encoding'],
feedback_strategy=params['conditions']['feedback_strategy'],
policy_architecture=params['conditions']['policy_architecture'],
goal_invention=params['conditions']['goal_invention'],
reward_checkpoint=params['conditions']['reward_checkpoint'],
rl_positive_ratio=params['conditions']['rl_positive_ratio'],
p_partner_availability=params['conditions']
['p_social_partner_availability'],
imagination_method=params['conditions']['imagination_method'],
git_commit='')
policy_language_model, reward_language_model = config.get_language_models(
params)
onehot_encoder = config.get_one_hot_encoder(params['all_descriptions'])
# Define the goal sampler for training
goal_sampler = GoalSampler(policy_language_model=policy_language_model,
reward_language_model=reward_language_model,
goal_dim=policy_language_model.goal_dim,
one_hot_encoder=onehot_encoder,
params=params)
reward_function = config.get_reward_function(goal_sampler, params)
if params['conditions']['reward_function'] == 'learned_lstm':
reward_function.restore_from_checkpoint(
PATH +
'reward_checkpoints/reward_func_checkpoint_{}'.format(EPOCH))
policy_language_model.set_reward_function(reward_function)
if reward_language_model is not None:
reward_language_model.set_reward_function(reward_function)
goal_sampler.update_discovered_goals(params['all_descriptions'],
episode_count=0,
epoch=0)
# Define learning algorithm
policy = config.configure_learning_algo(reward_function=reward_function,
goal_sampler=goal_sampler,
params=params)
policy.load_params(POLICY_FILE)
evaluation_worker = RolloutWorker(make_env=params['make_env'],
policy=policy,
reward_function=reward_function,
params=params,
render=render,
**params['evaluation_rollout_params'])
evaluation_worker.seed(seed)
# Run evaluation.
evaluation_worker.clear_history()
env_params = evaluation_worker.env.unwrapped.params
train_descriptions, test_descriptions, _ = generate_all_descriptions(
env_params)
train_descriptions = list(train_descriptions)
np.random.shuffle(list(test_descriptions))
np.random.shuffle(train_descriptions)
successes_test_descr = []
for d in test_descriptions:
successes_test_descr.append([])
print(d)
for i in range(n_test_rollouts):
goal_str = [d]
goal_encoding = [policy_language_model.encode(goal_str[0])]
goal_id = [0]
ep = evaluation_worker.generate_rollouts(
exploit=True,
imagined=False,
goals_str=goal_str,
goals_encodings=goal_encoding,
goals_ids=goal_id)
out = get_reward_from_state(ep[0]['obs'][-1], goal_str[0],
env_params)
successes_test_descr[-1].append(out == 1)
print('Success rate {}: {}'.format(d,
np.mean(successes_test_descr[-1])))
print('Global success rate: {}'.format(np.mean(successes_test_descr)))
def plot_generalization(path, freq=10):
first = True
trial_folder = path
for trial in os.listdir(path):
print(trial)
# if os.path.exists(path + '/' + trial + '/adaptation_success_rates_food.txt'):
trial_folder = path + '/' + trial + '/'
policy_folder = trial_folder + 'policy_checkpoints/'
params_file = trial_folder + 'params.json'
data = pd.read_csv(os.path.join(trial_folder, 'progress.csv'))
all_epochs = data['epoch']
all_episodes = data['episode']
epochs = []
episodes = []
for epoch, episode in zip(all_epochs, all_episodes):
if epoch % freq == 0:
epochs.append(epoch)
episodes.append(int(episode))
# Load params
with open(params_file) as json_file:
params = json.load(json_file)
seed = params['experiment_params']['seed']
set_global_seeds(seed)
goal_invention = int(
params['conditions']['goal_invention'].split('_')[-1])
env_id = params['conditions']['env_id']
if 'plant' not in env_id:
test_plants = plants.copy() + ['plant', 'living_thing']
test_plants.remove('flower')
test_descriptions = [
'Grow {} {}'.format(c, p) for c in thing_colors + ['any']
for p in test_plants
]
else:
if 'big' in env_id:
test_plants = [
'algae', 'bonsai', 'tree', 'bush', 'plant', 'living_thing'
]
else:
test_plants = ['tree', 'bush', 'plant', 'living_thing']
test_descriptions = [
'Grow {} {}'.format(c, p) for c in thing_colors + ['any']
for p in test_plants
]
first_epoch = True
rank = 0
if first:
if not RENDER:
env = 'PlaygroundNavigation-v1'
else:
env = 'PlaygroundNavigationRender-v1'
params, rank_seed = config.configure_everything(
rank=rank,
seed=seed,
num_cpu=params['experiment_params']['n_cpus'],
env=env,
trial_id=0,
n_epochs=10,
reward_function=params['conditions']['reward_function'],
curriculum_replay_target=params['conditions']
['curriculum_replay_target'],
curriculum_target=params['conditions']['curriculum_target'],
policy_encoding=params['conditions']['policy_encoding'],
bias_buffer=params['conditions']['bias_buffer'],
feedback_strategy=params['conditions']['feedback_strategy'],
goal_sampling_policy=params['conditions']
['goal_sampling_policy'],
policy_architecture=params['conditions']
['policy_architecture'],
goal_invention=params['conditions']['goal_invention'],
reward_checkpoint=params['conditions']['reward_checkpoint'],
rl_positive_ratio=params['conditions']['rl_positive_ratio'],
p_partner_availability=params['conditions']
['p_social_partner_availability'],
power_rarity=2,
git_commit='')
policy_language_model, reward_language_model = config.get_language_models(
params)
onehot_encoder = config.get_one_hot_encoder()
goal_sampler = GoalSampler(
policy_language_model=policy_language_model,
reward_language_model=reward_language_model,
goal_dim=policy_language_model.goal_dim,
one_hot_encoder=onehot_encoder,
**params['goal_sampler'],
params=params)
reward_function = config.get_reward_function(goal_sampler, params)
else:
def make_env():
return gym.make(params['conditions']['env_name'])
params['make_env'] = make_env
# Load policy.
success_rates = np.zeros([len(test_descriptions), len(epochs), 2])
for ind_ep, epoch in enumerate(epochs):
print('\n\n\t\t EPOCH', epoch)
if first:
first = False
reuse = False
else:
reuse = True
if params['conditions']['reward_function'] == 'learned_lstm':
reward_function.restore_from_checkpoint(
trial_folder +
'reward_checkpoints/reward_func_checkpoint_{}'.format(
epoch))
policy_language_model.set_reward_function(reward_function)
if reward_language_model is not None:
reward_language_model.set_reward_function(reward_function)
goal_sampler.update_discovered_goals(params['all_descriptions'],
episode_count=0,
epoch=0)
with tf.variable_scope(tf.get_variable_scope(), reuse=reuse):
with open(policy_folder + 'policy_{}.pkl'.format(epoch),
'rb') as f:
policy = pickle.load(f)
evaluation_worker = RolloutWorker(
make_env=params['make_env'],
policy=policy,
reward_function=reward_function,
params=params,
render=RENDER,
**params['evaluation_rollout_params'])
evaluation_worker.seed(seed)
# Run evaluation.
evaluation_worker.clear_history()
successes_per_descr = np.zeros([len(test_descriptions), 2])
for ind_inst, instruction in enumerate(test_descriptions):
# instruction = 'Grasp any fly'
success_instruction = []
goal_str = [instruction]
goal_encoding = [policy_language_model.encode(goal_str[0])]
goal_id = [0]
for i in range(N_REPET):
ep = evaluation_worker.generate_rollouts(
exploit=True,
imagined=False,
goals_str=goal_str,
goals_encodings=goal_encoding,
goals_ids=goal_id)
for t in range(ep[0]['obs'].shape[0]):
metric_food = food_on_furniture(
ep[0]['obs'][t], goal_str[0])
if metric_food:
# print('\n\n Touched food')
break
for t in range(ep[0]['obs'].shape[0]):
metric_water = water_on_furniture(
ep[0]['obs'][t], goal_str[0])
if metric_water:
# print('\n \n Touched water')
break
success_instruction.append([metric_food, metric_water])
success_instruction = np.array(success_instruction)
success_rate_inst = np.mean(success_instruction, axis=0)
successes_per_descr[ind_inst] = success_rate_inst
print('\t Success rate {}: food {}, water {}'.format(
goal_str[0], success_rate_inst[0], success_rate_inst[1]))
success_rates[ind_inst, ind_ep, :] = success_rate_inst
np.savetxt(trial_folder + 'adaptation_success_rates_water.txt',
success_rates[:, :, 1])
np.savetxt(trial_folder + 'adaptation_success_rates_food.txt',
success_rates[:, :, 0])
# success_rates = np.zeros([len(test_descriptions), len(epochs), 2])
# success_rates[:, :, 0] = np.loadtxt(trial_folder + 'adaptation_success_rates_food.txt')
# success_rates[:, :, 1] = np.loadtxt(trial_folder + 'adaptation_success_rates_water.txt')
line, err_min, err_max = get_stat_func(LINE, ERR)
# plot
fig = plt.figure(figsize=(22, 15), frameon=False)
ax = fig.add_subplot(111)
ax.spines['top'].set_linewidth(6)
ax.spines['right'].set_linewidth(6)
ax.spines['bottom'].set_linewidth(6)
ax.spines['left'].set_linewidth(6)
ax.tick_params(width=4, direction='in', length=10, labelsize='small')
for i in range(2):
plt.plot(np.array(episodes) / 1000,
line(success_rates)[:, i],
linewidth=10,
color=colors[i])
plt.fill_between(np.array(episodes) / 1000,
err_min(success_rates)[:, i],
err_max(success_rates)[:, i],
color=colors[i],
alpha=0.2)
# plt.vlines(goal_invention * 0.6, ymin=0, ymax=1, linestyles='--', color='k', linewidth=5)
leg = plt.legend(['food', 'water'], frameon=False)
lab = plt.xlabel('Episodes (x$10^3$)')
plt.ylim([-0.01, 1.01])
plt.yticks([0.25, 0.50, 0.75, 1])
lab2 = plt.ylabel('Average success rate')
plt.savefig(os.path.join(trial_folder, 'adaptation_success_rates.pdf'),
bbox_extra_artists=(lab, lab2, leg),
bbox_inches='tight',
dpi=50) # add leg
def run_generalization_study(path, freq=10):
first = True
for t_id, trial in enumerate(os.listdir(path)):
print(trial)
t_init = time.time()
trial_folder = path + '/' + trial + '/'
policy_folder = trial_folder + 'policy_checkpoints/'
params_file = trial_folder + 'params.json'
data = pd.read_csv(os.path.join(trial_folder, 'progress.csv'))
all_epochs = data['epoch']
all_episodes = data['episode']
epochs = []
episodes = []
for epoch, episode in zip(all_epochs, all_episodes):
if epoch % freq == 0:
epochs.append(epoch)
episodes.append(int(episode))
# Load params
with open(params_file) as json_file:
params = json.load(json_file)
seed = params['experiment_params']['seed']
set_global_seeds(seed)
goal_invention = int(params['conditions']['goal_invention'].split('_')[-1])
test_descriptions = params['test_descriptions']
rank = 0
if first:
if not RENDER:
env = 'PlaygroundNavigation-v1'
else:
env = 'PlaygroundNavigationRender-v1'
params, rank_seed = config.configure_everything(rank=rank,
seed=seed,
num_cpu=params['experiment_params']['n_cpus'],
env=env,
trial_id=0,
n_epochs=10,
reward_function=params['conditions']['reward_function'],
policy_encoding=params['conditions']['policy_encoding'],
bias_buffer=params['conditions']['bias_buffer'],
feedback_strategy=params['conditions']['feedback_strategy'],
policy_architecture=params['conditions']['policy_architecture'],
goal_invention=params['conditions']['goal_invention'],
reward_checkpoint=params['conditions']['reward_checkpoint'],
rl_positive_ratio=params['conditions']['rl_positive_ratio'],
p_partner_availability=params['conditions']['p_social_partner_availability'],
git_commit='')
policy_language_model, reward_language_model = config.get_language_models(params)
onehot_encoder = config.get_one_hot_encoder()
goal_sampler = GoalSampler(policy_language_model=policy_language_model,
reward_language_model=reward_language_model,
goal_dim=policy_language_model.goal_dim,
one_hot_encoder=onehot_encoder,
**params['goal_sampler'],
params=params)
reward_function = config.get_reward_function(goal_sampler, params)
else:
def make_env():
return gym.make(params['conditions']['env_name'])
params['make_env'] = make_env
loaded = False
success_rates = np.zeros([len(test_descriptions), len(epochs)])
if params['conditions']['reward_function'] == 'pretrained':
reward_function.load_params(trial_folder + 'params_reward')
if not loaded:
# Load policy.
t_init = time.time()
for ind_ep, epoch in enumerate(epochs):
print(time.time() - t_init)
t_init = time.time()
print('\n\n\t\t EPOCH', epoch)
if first:
first = False
reuse = False
else:
reuse = True
if params['conditions']['reward_function'] == 'learned_lstm':
reward_function.restore_from_checkpoint(trial_folder + 'reward_checkpoints/reward_func_checkpoint_{}'.format(epoch))
policy_language_model.set_reward_function(reward_function)
if reward_language_model is not None:
reward_language_model.set_reward_function(reward_function)
goal_sampler.update_discovered_goals(params['all_descriptions'], episode_count=0, epoch=0)
with tf.variable_scope(tf.get_variable_scope(), reuse=reuse):
with open(policy_folder + 'policy_{}.pkl'.format(epoch), 'rb') as f:
policy = pickle.load(f)
evaluation_worker = RolloutWorker(make_env=params['make_env'],
policy=policy,
reward_function=reward_function,
params=params,
render=RENDER,
**params['evaluation_rollout_params'])
evaluation_worker.seed(seed)
# Run evaluation.
evaluation_worker.clear_history()
successes_per_descr = np.zeros([len(test_descriptions)])
for ind_inst, instruction in enumerate(test_descriptions):
# instruction = 'Grasp any fly'
success_instruction = []
goal_str = [instruction]
goal_encoding = [policy_language_model.encode(goal_str[0])]
goal_id = [0]
for i in range(N_REPET):
ep = evaluation_worker.generate_rollouts(exploit=True,
imagined=False,
goals_str=goal_str,
goals_encodings=goal_encoding,
goals_ids=goal_id)
success = get_reward_from_state(state=ep[0]['obs'][-1], goal=instruction)
success_instruction.append(success)
success_rate_inst = np.mean(success_instruction)
successes_per_descr[ind_inst] = success_rate_inst
print('\t Success rate {}: {}'.format(goal_str[0], success_rate_inst))
success_rates[ind_inst, ind_ep] = success_rate_inst
np.savetxt(trial_folder + 'generalization_success_rates.txt', success_rates)