def train(agent, replay_buffer, dev_data, objective='mapo'):
"""Training Loop."""
sgd_steps = 0
train_env_dict = replay_buffer.env_dict
train_sample_gen = SampleGenerator(
replay_buffer,
agent,
objective=objective,
explore=FLAGS.explore,
n_samples=FLAGS.n_replay_samples,
use_top_k_samples=FLAGS.use_top_k_samples,
min_replay_weight=FLAGS.min_replay_weight)
train_sample_generator = train_sample_gen.generate_samples(
batch_size=len(train_env_dict), debug=FLAGS.is_debug)
if FLAGS.meta_learn:
dev_replay_buffer = dev_data
dev_env_dict = dev_replay_buffer.env_dict
dev_sample_gen = SampleGenerator(
dev_replay_buffer,
agent,
objective=objective,
explore=FLAGS.dev_explore)
dev_sample_generator = dev_sample_gen.generate_samples(
batch_size=len(dev_env_dict), debug=FLAGS.is_debug)
else:
dev_env_dict = dev_data
ckpt_dir = osp.join(FLAGS.train_dir, 'model')
if (tf.train.latest_checkpoint(ckpt_dir) is
None) and FLAGS.pretrained_ckpt_dir:
pretrained_ckpt_dir = osp.join(FLAGS.pretrained_ckpt_dir, 'best_model')
# Store weights before loading the checkpoint
if FLAGS.pretrained_load_data_only and FLAGS.meta_learn:
pi_weights = agent.pi.get_weights()
create_checkpoint_manager(
agent,
pretrained_ckpt_dir,
restore=True,
include_optimizer=False,
meta_learn=False)
# Reset the global step to 0
tf.assign(agent.global_step, 0)
if FLAGS.pretrained_load_data_only and FLAGS.meta_learn:
dev_trajs = agent.sample_trajs(dev_env_dict.values(), greedy=True)
dev_replay_buffer.save_trajs(dev_trajs)
agent.pi.set_weights(pi_weights)
tf.logging.info('Collected data using the pretrained checkpoint')
ckpt_manager = create_checkpoint_manager(
agent,
ckpt_dir,
restore=True,
include_optimizer=True,
meta_learn=FLAGS.meta_learn)
best_ckpt_dir = osp.join(FLAGS.train_dir, 'best_model')
best_ckpt_manager = create_checkpoint_manager(
agent, best_ckpt_dir, restore=False, include_optimizer=False)
# Log summaries for the accuracy results
summary_writer = contrib_summary.create_file_writer(
osp.join(FLAGS.train_dir, 'tb_log'), flush_millis=5000)
max_val_acc = helpers.eval_agent(agent, dev_env_dict)
with summary_writer.as_default(), \
contrib_summary.always_record_summaries():
while agent.global_step.numpy() < FLAGS.num_steps:
if sgd_steps % FLAGS.save_every_n == 0:
ckpt_manager.save()
train_acc = helpers.eval_agent(agent, train_env_dict)
val_acc = helpers.eval_agent(agent, dev_env_dict)
contrib_summary.scalar('train_acc', train_acc)
contrib_summary.scalar('validation_acc', val_acc)
if val_acc > max_val_acc:
max_val_acc = val_acc
tf.logging.info('Best validation accuracy {}'.format(max_val_acc))
best_ckpt_manager.save()
# Sample environments and trajectories
samples, contexts = next(train_sample_generator)
if FLAGS.meta_learn:
dev_samples, dev_contexts = next(dev_sample_generator)
agent.update(samples, contexts, dev_samples, dev_contexts)
else:
# Update the policy
agent.update(samples, contexts)
# Update the random noise
agent.update_eps(agent.global_step.numpy(), FLAGS.num_steps)
sgd_steps += 1
def run_experiment():
"""Code for creating the agent and run training/evaluation."""
agent_args = dict(
log_summaries=FLAGS.log_summaries,
eps=FLAGS.eps,
entropy_reg_coeff=FLAGS.entropy_reg_coeff,
units=FLAGS.units,
learning_rate=FLAGS.learning_rate,
debug=FLAGS.is_debug,
seed=FLAGS.seed,
gamma=FLAGS.gamma,
use_critic=False,
max_grad_norm=FLAGS.max_grad_norm,
objective='mapo')
if FLAGS.meta_learn:
agent = MetaRLAgent(
meta_lr=FLAGS.meta_lr, score_fn=FLAGS.score_fn, **agent_args)
else:
agent = RLAgent(**agent_args)
if FLAGS.use_buffer_scorer:
num_features = len(common_flags.PAIR_FEATURE_KEYS)
score_weights = np.zeros(
num_features * (num_features + 1), dtype=np.float32)
w1, w2 = [
getattr(FLAGS, 'score_{}'.format(x))
for x in common_flags.PAIRWISE_WEIGHTS
]
for counter, key in enumerate(common_flags.PAIR_FEATURE_KEYS):
# Assign the weights to the first `num_features`
score_weights[counter] = getattr(FLAGS, 'score_{}'.format(key))
for counter2, key2 in enumerate(common_flags.PAIR_FEATURE_KEYS):
index = (counter + 1) * num_features + counter2
interactions = helpers.cross_product(key, key2)
features = [getattr(FLAGS, 'score_{}'.format(i)) for i in interactions]
# Pairwise interaction features are assumed to be a linear combination
# of unary interaction features
score_weights[index] = features[0] * features[-1] * w1 + w2 * features[
1] * features[2]
buffer_scorer = BufferScorer(score_weights)
else:
buffer_scorer = None
if not FLAGS.eval_only: # Training
train_replay_buffer = helpers.create_replay_buffer(
FLAGS.train_file,
grid_size=FLAGS.grid_size,
n_plants=FLAGS.n_train_plants,
num_envs=FLAGS.n_train_envs,
seed=FLAGS.seed,
use_gold_trajs=FLAGS.use_gold_trajs,
buffer_scorer=buffer_scorer)
if not FLAGS.meta_learn:
dev_env_dict = helpers.create_dataset(
FLAGS.dev_file,
grid_size=FLAGS.grid_size,
n_plants=FLAGS.n_dev_plants,
seed=FLAGS.seed,
num_envs=FLAGS.n_dev_envs,
return_trajs=False)
train(agent, train_replay_buffer, dev_env_dict)
else:
dev_replay_buffer = helpers.create_replay_buffer(
FLAGS.dev_file,
grid_size=FLAGS.grid_size,
n_plants=FLAGS.n_dev_plants,
seed=FLAGS.seed,
use_gold_trajs=FLAGS.use_dev_gold_trajs,
save_trajs=not (FLAGS.pretrained_ckpt_dir and FLAGS.dev_explore),
num_envs=FLAGS.n_dev_envs)
train(agent, train_replay_buffer, dev_replay_buffer)
best_ckpt_dir = osp.join(FLAGS.train_dir, 'best_model')
else:
best_ckpt_dir = osp.join(FLAGS.eval_dir, 'best_model')
# Run the agent evaluation at the end
test_env_dict = helpers.create_dataset(
FLAGS.test_file,
grid_size=FLAGS.grid_size,
n_plants=FLAGS.n_test_plants,
return_trajs=False,
num_envs=None,
seed=FLAGS.seed)
create_checkpoint_manager(
agent, best_ckpt_dir, restore=True, include_optimizer=False)
test_accuracy = helpers.eval_agent(agent, test_env_dict)
tf.logging.info('Final Test accuracy {}'.format(test_accuracy))
