Metadata-Version: 1.1
Name: tfrddlsim
Version: 0.6.7
Summary: RDDL2TensorFlow parser, compiler, and simulator.
Home-page: https://github.com/thiagopbueno/tf-rddlsim
Author: Thiago P. Bueno
Author-email: thiago.pbueno@gmail.com
License: GNU General Public License v3.0
Description: # tf-rddlsim [![Build Status](https://travis-ci.org/thiagopbueno/tf-rddlsim.svg?branch=master)](https://travis-ci.org/thiagopbueno/tf-rddlsim) [![License](https://img.shields.io/aur/license/yaourt.svg)](https://github.com/thiagopbueno/tf-rddlsim/blob/master/LICENSE)
        
        RDDL2TensorFlow compiler and trajectory simulator in Python3.
        
        # Quickstart
        
        ```text
        $ pip3 install tfrddlsim
        ```
        
        # Usage
        
        tf-rddlsim can be used as a standalone script or programmatically.
        
        
        ## Script mode
        
        ```text
        $ usage: tfrddlsim [-h] (--file FILE | --rddl RDDL) [--policy {default,random}]
                         [--viz {generic,navigation}] [-hr HORIZON] [-b BATCH_SIZE]
                         [-v]
        
        RDDL2TensorFlow compiler and simulator
        
        optional arguments:
          -h, --help            show this help message and exit
          --file FILE           RDDL filepath
          --rddl RDDL           RDDL domain id
          --policy {default,random}
                                type of policy (default=random)
          --viz {generic,navigation}
                                type of visualizer (default=generic)
          -hr HORIZON, --horizon HORIZON
                                number of timesteps of each trajectory (default=40)
          -b BATCH_SIZE, --batch_size BATCH_SIZE
                                number of trajectories in a batch (default=75)
          -v, --verbose         verbosity mode
        ```
        
        
        ## Programmatic mode
        
        ```python
        import rddlgym
        
        from tfrddlsim.policy import RandomPolicy
        from tfrddlsim.simulation.policy_simulator import PolicySimulator
        from tfrddlsim.viz import GenericVisualizer
        
        # parse and compile RDDL
        rddl2tf = rddlgym.make('Reservoir-8')
        rddl2tf.batch_mode_on()
        
        # run simulations
        horizon = 40
        batch_size = 75
        policy = RandomPolicy(rddl2tf, batch_size)
        simulator = Simulator(rddl2tf, policy, batch_size)
        trajectories = simulator.run(horizon)
        
        # visualize trajectories
        viz = GenericVisualizer(rddl2tf, verbose=True)
        viz.render(trajectories)
        ```
        
        # Compiler
        
        ## Parameterized Variables (pvariables)
        
        Each RDDL fluent is compiled to a ``tfrddlsim.TensorFluent`` after instantiation.
        
        A ``tfrddlsim.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 ``tfrddlsim.TensorFluentShape`` object (the rank of a ``tfrddlsim.TensorFluent`` corresponds to the fluent arity and the size of its dimensions corresponds to the number of objects of each type). Also, a ``tfrddlsim.TensorFluentShape`` manages batch sizes when evaluating operations in batch mode.
        
        Additionally, a ``tfrddlsim.TensorFluent``keeps information about the ordering of the fluent parameters in a ``tfrddlsim.TensorScope`` object.
        
        The ``tfrddlsim.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 ``tfrddlsim.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 ``tfrddlsim.Simulator``.
        
        
        # Simulator
        
        The ``tfrddlsim.Simulator`` implements a stochastic Recurrent Neural Net (RNN) in order to sample state-action trajectories. Each RNN cell encapsulates a ``tfrddlsim.Policy`` module generating actions for current states and comprehends the transition (specified by the CPFs) and reward functions. Sampling is done through dynamic unrolling of the RNN model with the embedded ``tfrddlsim.Policy``.
        
        Note that the ``tfrddlsim`` package only provides a ``tfrddlsim.RandomPolicy`` and a ``tfrddlsim.DefaultPolicy`` (constant policy with all action fluents with default values).
        
        
        # License
        
        Copyright (c) 2018 Thiago Pereira Bueno All Rights Reserved.
        
        tf-rddlsim 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.
        
        tf-rddlsim 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 tf-rddlsim. If not, see http://www.gnu.org/licenses/.
        
Keywords: rddl,tensorflow,probabilistic-planning,mdp,simulator
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
