Metadata-Version: 1.2
Name: rddl2tf
Version: 0.4.8
Summary: RDDL2TensorFlow compiler.
Home-page: https://github.com/thiagopbueno/rddl2tf
Author: Thiago P. Bueno
Author-email: thiago.pbueno@gmail.com
License: GNU General Public License v3.0
Description: # rddl2tf [![Build Status](https://travis-ci.org/thiagopbueno/rddl2tf.svg?branch=master)](https://travis-ci.org/thiagopbueno/rddl2tf) [![Documentation Status](https://readthedocs.org/projects/rddl2tf/badge/?version=latest)](https://rddl2tf.readthedocs.io/en/latest/?badge=latest) [![License](https://img.shields.io/aur/license/yaourt.svg)](https://github.com/thiagopbueno/rddl2tf/blob/master/LICENSE)
        
        RDDL2TensorFlow compiler in Python3.
        
        # Quickstart
        
        **rddl2tf** is a Python 3.5+ package available in PyPI.
        
        ```text
        $ pip3 install rddl2tf
        ```
        
        
        # Usage
        
        rddl2tf can be used as a standalone script or programmatically.
        
        
        ## Script mode
        
        ```text
        $ rddl2tf --help
        usage: rddl2tf [-h] [-b BATCH_SIZE] [--logdir LOGDIR] rddl
        
        rddl2tf (v0.4.7): 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)
        ```
        
        ### Examples
        
        ```text
        $ rddl2tf Reservoir-8 --batch-size=1024 --logdir=/tmp/rddl2tf
        tensorboard --logdir /tmp/rddl2tf/reservoir/inst_reservoir_res8
        ```
        
        ```text
        $ rddl2tf Mars_Rover --batch-size=1024 --logdir=/tmp/rddl2tf
        tensorboard --logdir /tmp/rddl2tf/simple_mars_rover/inst_simple_mars_rover_pics3
        ```
        
        
        ## Programmatic mode
        
        ```python
        import rddlgym
        
        from rddl2tf.compiler import Compiler
        
        # parse and compile RDDL
        model_id = 'Reservoir-8'
        model = rddlgym.make(model_id, mode=rddlgym.AST)
        compiler = Compiler(model)
        
        # set batch mode
        compiler.batch_mode_on()
        batch_size = 256
        
        # compile initial state and default action fluents
        state = compiler.compile_initial_state(batch_size)
        action = compiler.compile_default_action(batch_size)
        
        # compile state invariants and action preconditions
        invariants = compiler.compile_state_invariants(state)
        preconditions = compiler.compile_action_preconditions(state, action)
        
        # compile intermediate fluents and next state fluents
        scope = compiler.transition_scope(state, action)
        interms, next_state = compiler.compile_cpfs(scope, batch_size)
        
        # compile reward function
        scope.update(next_state)
        reward = compiler.compile_reward(scope)
        ```
        
        
        # Compiler
        
        
        ## Parameterized Variables (pvariables)
        
        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.TensorFluent``keeps 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.
        
        
        ## Conditional Probability Functions (CPFs)
        
        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``.
        
        
        # Documentation
        
        Please refer to [https://rddl2tf.readthedocs.io/](https://rddl2tf.readthedocs.io/en/latest/) for the code documentation.
        
        
        # Support
        
        If you are having issues with ``rddl2tf``, please let me know at: [thiago.pbueno@gmail.com](mailto://thiago.pbueno@gmail.com).
        
        
        # License
        
        Copyright (c) 2018 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/.
        
Keywords: rddl,tensorflow
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.5
