def main(argv):
del argv
if FLAGS.hparams is not None:
with open(FLAGS.hparams, 'r') as f:
hparams = deep_q_networks.get_hparams(**json.load(f))
else:
hparams = deep_q_networks.get_hparams()
environment = Molecule(
atom_types=set(hparams.atom_types),
init_mol=FLAGS.start_molecule,
allow_removal=hparams.allow_removal,
allow_no_modification=hparams.allow_no_modification,
allow_bonds_between_rings=hparams.allow_bonds_between_rings,
allowed_ring_sizes=set(hparams.allowed_ring_sizes),
max_steps=hparams.max_steps_per_episode)
dqn = deep_q_networks.DeepQNetwork(
input_shape=(hparams.batch_size, hparams.fingerprint_length + 1),
q_fn=functools.partial(deep_q_networks.multi_layer_model,
hparams=hparams),
optimizer=hparams.optimizer,
grad_clipping=hparams.grad_clipping,
num_bootstrap_heads=hparams.num_bootstrap_heads,
gamma=hparams.gamma,
epsilon=1.0)
run_dqn.run_training(
hparams=hparams,
environment=environment,
dqn=dqn,
)
core.write_hparams(hparams, os.path.join(FLAGS.model_dir, 'config.json'))
def main(argv):
del argv # unused.
if FLAGS.hparams is not None:
with gfile.Open(FLAGS.hparams, 'r') as f:
hparams = deep_q_networks.get_hparams(**json.load(f))
else:
hparams = deep_q_networks.get_hparams()
filename = 'all_800_mols.json'
with gfile.Open(filename) as fp:
all_molecules = json.load(fp)
environment = LogPRewardWithSimilarityConstraintMolecule(
similarity_constraint=FLAGS.similarity_constraint,
discount_factor=hparams.discount_factor,
all_molecules=all_molecules,
atom_types=set(hparams.atom_types),
init_mol=None,
allow_removal=hparams.allow_removal,
allow_no_modification=hparams.allow_no_modification,
allow_bonds_between_rings=hparams.allow_bonds_between_rings,
allowed_ring_sizes=set(hparams.allowed_ring_sizes),
max_steps=hparams.max_steps_per_episode)
dqn = deep_q_networks.DeepQNetwork(
input_shape=(hparams.batch_size, hparams.fingerprint_length + 1),
q_fn=functools.partial(deep_q_networks.multi_layer_model,
hparams=hparams),
optimizer=hparams.optimizer,
grad_clipping=hparams.grad_clipping,
num_bootstrap_heads=hparams.num_bootstrap_heads,
gamma=hparams.gamma,
epsilon=1.0)
run_dqn.run_training(
hparams=hparams,
environment=environment,
dqn=dqn,
)
core.write_hparams(hparams, os.path.join(FLAGS.model_dir, 'config.json'))
def run_dqn(multi_objective=False):
"""Run the training of Deep Q Network algorithm.
Args:
multi_objective: Boolean. Whether to run the multiobjective DQN.
"""
if FLAGS.hparams is not None:
with gfile.Open(FLAGS.hparams, 'r') as f:
hparams = deep_q_networks.get_hparams(**json.load(f))
else:
hparams = deep_q_networks.get_hparams()
logging.info(
'HParams:\n%s', '\n'.join([
'\t%s: %s' % (key, value)
for key, value in sorted(hparams.values().items())
]))
# TODO(zzp): merge single objective DQN to multi objective DQN.
if multi_objective:
environment = MultiObjectiveRewardMolecule(
target_molecule=FLAGS.target_molecule,
atom_types=set(hparams.atom_types),
init_mol=FLAGS.start_molecule,
allow_removal=hparams.allow_removal,
allow_no_modification=hparams.allow_no_modification,
allow_bonds_between_rings=False,
allowed_ring_sizes={3, 4, 5, 6},
max_steps=hparams.max_steps_per_episode)
dqn = deep_q_networks.MultiObjectiveDeepQNetwork(
objective_weight=np.array([[FLAGS.similarity_weight],
[1 - FLAGS.similarity_weight]]),
input_shape=(hparams.batch_size, hparams.fingerprint_length + 1),
q_fn=functools.partial(deep_q_networks.multi_layer_model,
hparams=hparams),
optimizer=hparams.optimizer,
grad_clipping=hparams.grad_clipping,
num_bootstrap_heads=hparams.num_bootstrap_heads,
gamma=hparams.gamma,
epsilon=1.0)
else:
environment = TargetWeightMolecule(
target_weight=FLAGS.target_weight,
atom_types=set(hparams.atom_types),
init_mol=FLAGS.start_molecule,
allow_removal=hparams.allow_removal,
allow_no_modification=hparams.allow_no_modification,
allow_bonds_between_rings=hparams.allow_bonds_between_rings,
allowed_ring_sizes=set(hparams.allowed_ring_sizes),
max_steps=hparams.max_steps_per_episode)
dqn = deep_q_networks.DeepQNetwork(
input_shape=(hparams.batch_size, hparams.fingerprint_length + 1),
q_fn=functools.partial(deep_q_networks.multi_layer_model,
hparams=hparams),
optimizer=hparams.optimizer,
grad_clipping=hparams.grad_clipping,
num_bootstrap_heads=hparams.num_bootstrap_heads,
gamma=hparams.gamma,
epsilon=1.0)
run_training(
hparams=hparams,
environment=environment,
dqn=dqn,
)
core.write_hparams(hparams, os.path.join(FLAGS.model_dir, 'config.json'))
