def setUpClass(cls):
with open('rddl/Reservoir.rddl', mode='r') as file:
RESERVOIR = file.read()
with open('rddl/Mars_Rover.rddl', mode='r') as file:
MARS_ROVER = file.read()
parser = RDDLParser()
parser.build()
cls.rddl1 = parser.parse(RESERVOIR)
cls.rddl1.build()
cls.rddl2 = parser.parse(MARS_ROVER)
cls.rddl2.build()
cls.rddls = [cls.rddl1, cls.rddl2]
def _load_rddl_model(self, filename):
with open(filename, 'r') as input_file:
rddl = input_file.read()
parser = RDDLParser()
parser.build()
# parse RDDL
return parser.parse(rddl)
def parse_model(filename, verbose=False):
rddl = read_model(filename)
parser = RDDLParser(verbose=verbose)
parser.build()
model = parser.parse(rddl)
model.build()
return model
def setUpClass(cls):
# initialize hyper-parameters
cls.horizon = 40
cls.batch_size = 64
cls.epochs = 50
cls.learning_rate = 0.01
# parse RDDL file
with open('rddl/deterministic/Navigation.rddl') as file:
parser = RDDLParser()
parser.build()
rddl = parser.parse(file.read())
rddl.build()
# initializer RDDL2TensorFlow compiler
cls.rddl2tf = Compiler(rddl, batch_mode=True)
# initialize open-loop policy
cls.policy = OpenLoopPolicy(cls.rddl2tf, cls.batch_size, cls.horizon)
cls.policy.build('test')
# initialize ActionOptimizer
cls.optimizer = ActionOptimizer(cls.rddl2tf, cls.policy)
cls.optimizer.build(cls.learning_rate, cls.batch_size, cls.horizon)
def setUpClass(cls):
# initialize hyper-parameters
cls.horizon = 40
cls.batch_size = 64
# parse RDDL file
with open('rddl/deterministic/Navigation.rddl') as file:
parser = RDDLParser()
parser.build()
rddl = parser.parse(file.read())
rddl.build()
# initializer RDDL2TensorFlow compiler
cls.rddl2tf = Compiler(rddl, batch_mode=True)
# initialize open-loop policy
cls.policy = OpenLoopPolicy(cls.rddl2tf, cls.batch_size, cls.horizon)
cls.policy.build('test')
# execute policy for the given horizon and initial state
with cls.rddl2tf.graph.as_default():
cls.state = cls.rddl2tf.compile_initial_state(cls.batch_size)
cls.actions = []
for t in range(cls.horizon - 1, -1, -1):
timestep = tf.constant(t,
dtype=tf.float32,
shape=(cls.batch_size, 1))
action = cls.policy(cls.state, timestep)
cls.actions.append(action)
def setUpClass(cls):
# initialize hyper-parameters
cls.horizon = 40
cls.batch_size = 1
# parse RDDL file
with open('rddl/deterministic/Navigation.rddl') as file:
parser = RDDLParser()
parser.build()
rddl = parser.parse(file.read())
rddl.build()
# initializer RDDL2TensorFlow compiler
cls.rddl2tf = Compiler(rddl, batch_mode=True)
# initialize open-loop policy
cls.policy = OpenLoopPolicy(cls.rddl2tf, cls.batch_size, cls.horizon)
cls.policy.build('test')
# sample policy variables to initialize open-loop policy
cls.policy_variables = []
for shape in cls.rddl2tf.rddl.action_size:
size = [cls.horizon] + list(shape)
cls.policy_variables.append(
np.random.uniform(low=-1.0, high=1.0, size=size))
# initialize action evaluator
cls.evaluator = ActionEvaluator(cls.rddl2tf, cls.policy)
def parse_model(filename, verbose=False):
"""Returns RDDL abstract syntax tree (AST)."""
rddl = read_model(filename)
parser = RDDLParser(verbose=verbose)
parser.build()
model = parser.parse(rddl)
model.build()
return model
def parse_rddl(rddl: str, verbose: bool) -> RDDL:
"""
Parses an RDDL string definition into a python object.
:param str rddl: the RDDL definition.
:param bool verbose: whether to print parse messages to the screen.
:rtype: RDDL
:return: the pyrddl python object created by parsing the given RDDL definition.
"""
# parse RDDL
rddl_parser = RDDLParser(verbose=verbose)
# rddl_parser.debugging = verbose
rddl_parser.build()
return rddl_parser.parse(rddl)