def define_simulation_graph(batch_env, algo_cls, config):
"""Define the algortihm and environment interaction.
Simulate a single step in all environments. See more details in `tools.simulate`.
Args:
batch_env: In-graph environments object.
algo_cls: Constructor of a batch algorithm.
config: Configuration object for the algorithm.
Returns:
Object providing graph elements via attributes.
"""
# pylint: disable=unused-variable
step = tf.Variable(0, False, dtype=tf.int32, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
should_log = tf.placeholder(tf.bool, name='should_log')
do_report = tf.placeholder(tf.bool, name='do_report')
force_reset = tf.placeholder(tf.bool, name='force_reset')
algo = algo_cls(batch_env, step, is_training, should_log, config)
should_step = tf.placeholder(tf.bool, name='should_step')
use_external_action = tf.placeholder(tf.bool, name='use_external_action')
external_action = tf.placeholder(batch_env.action_info[0],
shape=batch_env.action_info[1],
name='external_action')
done, score, summary = tools.simulate(batch_env, algo, should_step,
use_external_action, external_action,
should_log, force_reset)
message = 'Graph contains {} trainable variables.'
tf.logging.info(message.format(tools.count_weights()))
# pylint: enable=unused-variable
return tools.AttrDict(locals())
def define_simulation_graph(batch_env, algo_cls, config):
"""Define the algortihm and environment interaction.
Args:
batch_env: In-graph environments object.
algo_cls: Constructor of a batch algorithm.
config: Configuration object for the algorithm.
Returns:
Object providing graph elements via attributes.
"""
# pylint: disable=unused-variable
step = tf.Variable(0, False, dtype=tf.int32, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
should_log = tf.placeholder(tf.bool, name='should_log')
do_report = tf.placeholder(tf.bool, name='do_report')
force_reset = tf.placeholder(tf.bool, name='force_reset')
# Extended
is_optimizing_offense = tf.placeholder(tf.bool,
name='is_optimizing_offense')
algo = algo_cls(batch_env,
step,
is_training,
should_log,
config,
is_optimizing_offense=is_optimizing_offense)
done, score, summary, gail_summary = tools.simulate(
batch_env, algo, should_log, force_reset)
message = 'Graph contains {} trainable variables.'
tf.logging.info(message.format(tools.count_weights()))
# pylint: enable=unused-variable
return tools.AttrDict(locals())
def test_done_automatic(self):
batch_env = self._create_test_batch_env((1, 2, 3, 4))
algo = tools.MockAlgorithm(batch_env)
done, _, _ = tools.simulate(batch_env, algo, log=False, reset=False)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
self.assertAllEqual([True, False, False, False], sess.run(done))
self.assertAllEqual([True, True, False, False], sess.run(done))
self.assertAllEqual([True, False, True, False], sess.run(done))
self.assertAllEqual([True, True, False, True], sess.run(done))
def test_reset_automatic(self):
batch_env = self._create_test_batch_env((1, 2, 3, 4))
algo = tools.MockAlgorithm(batch_env)
done, _, _ = tools.simulate(batch_env, algo, log=False, reset=False)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
for _ in range(10):
sess.run(done)
self.assertAllEqual([1, 1, 1, 1, 1, 1, 1, 1, 1, 1], batch_env[0].steps)
self.assertAllEqual([2, 2, 2, 2, 2], batch_env[1].steps)
self.assertAllEqual([3, 3, 3, 1], batch_env[2].steps)
self.assertAllEqual([4, 4, 2], batch_env[3].steps)
def test_done_forced(self):
reset = tf.placeholder_with_default(False, ())
batch_env = self._create_test_batch_env((2, 4))
algo = tools.MockAlgorithm(batch_env)
done, _, _ = tools.simulate(batch_env, algo, False, reset)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
self.assertAllEqual([False, False], sess.run(done))
self.assertAllEqual([False, False], sess.run(done, {reset: True}))
self.assertAllEqual([True, False], sess.run(done))
self.assertAllEqual([False, False], sess.run(done, {reset: True}))
self.assertAllEqual([True, False], sess.run(done))
self.assertAllEqual([False, False], sess.run(done))
self.assertAllEqual([True, True], sess.run(done))
def define_simulation_graph(batch_env, algo_cls, config):
"""Define the algorithm and environment interaction.
Args:
batch_env: In-graph environments object.
algo_cls: Constructor of a batch algorithm.
config: Configuration object for the algorithm.
Returns:
Object providing graph elements via attributes.
"""
# pylint: disable=unused-variable
step = tf.Variable(0, False, dtype=tf.int32, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
should_log = tf.placeholder(tf.bool, name='should_log')
do_report = tf.placeholder(tf.bool, name='do_report')
force_reset = tf.placeholder(tf.bool, name='force_reset')
algo = algo_cls(batch_env, step, is_training, should_log, config)
done, score, summary = tools.simulate(
batch_env, algo, should_log, force_reset)
message = 'Graph contains {} trainable variables.'
tf.logging.info(message.format(tools.count_weights()))
# pylint: enable=unused-variable
return tools.AttrDict(locals())