https://github.com/thiagopbueno/rddl2tf
RDDL2TensorFlow compiler.
https://github.com/thiagopbueno/rddl2tf
rddl tensorflow
Last synced: 6 months ago
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RDDL2TensorFlow compiler.
- Host: GitHub
- URL: https://github.com/thiagopbueno/rddl2tf
- Owner: thiagopbueno
- License: gpl-3.0
- Created: 2018-09-11T13:21:09.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2020-11-16T16:33:41.000Z (over 5 years ago)
- Last Synced: 2025-09-29T10:25:45.852Z (9 months ago)
- Topics: rddl, tensorflow
- Language: Python
- Homepage: https://rddl2tf.readthedocs.io/
- Size: 151 KB
- Stars: 6
- Watchers: 1
- Forks: 6
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# rddl2tf [](https://travis-ci.org/thiagopbueno/rddl2tf) [](https://rddl2tf.readthedocs.io/en/latest/?badge=latest) [](https://github.com/thiagopbueno/rddl2tf/blob/master/LICENSE.txt)
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 (v0.5.1): 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)
```
### 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.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))
```
# Compiler
## Core API methods
- `rddl2tf.Compiler.initial_state`
- `rddl2tf.Compiler.default_action`
- `rddl2tf.Compiler.cpfs`
- `rddl2tf.Compiler.reward`
- `rddl2tf.Compiler.state_action_constraints`
- `rddl2tf.Compiler.action_preconditions`
- `rddl2tf.Compiler.state_invariants`
- `rddl2tf.Compiler.action_bound_constraints`
## 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) 2018-2020 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/.