RDDL2TensorFlow compiler in Python3.
rddl2tf is a Python 3.5+ package available in PyPI.
$ pip3 install rddl2tf
rddl2tf can be used as a standalone script or programmatically.
$ rddl2tf --help
usage: rddl2tf [-h] [-b BATCH_SIZE] [--logdir LOGDIR] rddl
rddl2tf (v0.5.1): RDDL2TensorFlow compiler in Python3.
positional arguments:
rddl path to RDDL file or rddlgym problem id
optional arguments:
-h, --help show this help message and exit
-b BATCH_SIZE, --batch-size BATCH_SIZE
number of fluents in a batch (default=256)
--logdir LOGDIR log directory for tensorboard graph visualization
(default=/tmp/rddl2tf)
$ rddl2tf Reservoir-8 --batch-size=1024 --logdir=/tmp/rddl2tf
tensorboard --logdir /tmp/rddl2tf/reservoir/inst_reservoir_res8
$ rddl2tf Mars_Rover --batch-size=1024 --logdir=/tmp/rddl2tf
tensorboard --logdir /tmp/rddl2tf/simple_mars_rover/inst_simple_mars_rover_pics3
import rddlgym
from rddl2tf.compilers import DefaultCompiler
# parse RDDL into an AST
model_id = 'Reservoir-8'
model = rddlgym.make(model_id, mode=rddlgym.AST)
# create a RDDL-to-TF compiler
compiler = DefaultCompiler(model, batch_size=256)
compiler.init()
# compile initial state and default action fluents
state = compiler.initial_state()
action = compiler.default_action()
# compile state invariants and action preconditions
invariants = compiler.state_invariants(state)
preconditions = compiler.action_preconditions(state, action)
# compile action bounds
bounds = compiler.action_bound_constraints(state)
# compile intermediate fluents and next state fluents
interms, next_state = compiler.cpfs(state, action)
# compile reward function
reward = compiler.reward(state, action, next_state)
# save and visualize the computation graph
logdir = os.path.join(args.logdir, model.domain.name, model.instance.name)
file_writer = tf.summary.FileWriter(logdir, compiler.graph)
print('tensorboard --logdir {}\n'.format(logdir))rddl2tf.Compiler.initial_staterddl2tf.Compiler.default_actionrddl2tf.Compiler.cpfsrddl2tf.Compiler.rewardrddl2tf.Compiler.state_action_constraintsrddl2tf.Compiler.action_preconditionsrddl2tf.Compiler.state_invariantsrddl2tf.Compiler.action_bound_constraints
Each RDDL fluent is compiled to a rddl2tf.TensorFluent after instantiation.
A rddl2tf.TensorFluent object wraps a tf.Tensor object. The arity and the number of objects corresponding to the type of each parameter of a fluent are reflected in a rddl2tf.TensorFluentShape object (the rank of a rddl2tf.TensorFluent corresponds to the fluent arity and the size of its dimensions corresponds to the number of objects of each type). Also, a rddl2tf.TensorFluentShape manages batch sizes when evaluating operations in batch mode.
Additionally, a rddl2tf.TensorFluentkeeps information about the ordering of the fluent parameters in a rddl2tf.TensorScope object.
The rddl2tf.TensorFluent abstraction is necessary in the evaluation of RDDL expressions due the broadcasting rules of operations in TensorFlow.
Each CPF expression is compiled into an operation in a tf.Graph, possibly composed of many other operations. Typical RDDL operations, functions, and probability distributions are mapped to equivalent TensorFlow ops. These operations are added to a tf.Graph by recursively compiling the expressions in a CPF into wrapped operations and functions implemented at the rddl2tf.TensorFluent level.
Note that the RDDL2TensorFlow compiler currently only supports element-wise operations (e.g. a(?x, ?y) = b(?x) * c(?y) is not allowed). However, all compiled operations are vectorized, i.e., computations are done simultaneously for all object instantiations of a pvariable.
Optionally, during simulation operations can be evaluated in batch mode. In this case, state-action trajectories are generated in parallel by the rddl2tf.Simulator.
Please refer to https://rddl2tf.readthedocs.io/ for the code documentation.
If you are having issues with rddl2tf, please let me know at: [email protected].
Copyright (c) 2018-2020 Thiago Pereira Bueno All Rights Reserved.
rddl2tf is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
rddl2tf is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with rddl2tf. If not, see http://www.gnu.org/licenses/.