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RDDL2TensorFlow parser, compiler, and simulator.

Project description

tf-rddlsim Build Status Documentation Status License

RDDL2TensorFlow compiler and trajectory simulator in Python3.

Quickstart

$ pip3 install tfrddlsim

Usage

tf-rddlsim can be used as a standalone script or programmatically.

Script mode

$ tfrddlsim --help

usage: tfrddlsim [-h] [--policy {default,random}] [--viz {generic,navigation}]
                 [-hr HORIZON] [-b BATCH_SIZE] [-v]
                 rddl

RDDL2TensorFlow compiler and simulator

positional arguments:
  rddl                  path to RDDL file or rddlgym problem id

optional arguments:
  -h, --help            show this help message and exit
  --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
$ tfrddlsim Navigation-v1 --policy random --viz navigation -hr 50 -b 32 -v
$ tfrddlsim Reservoir-8 --policy default --viz generic -hr 20 -b 128 -v

Programmatic mode

import rddlgym
from rddl2tf.compilers import DefaultCompiler as Compiler
from tfrddlsim.policy import RandomPolicy
from tfrddlsim.simulation.policy_simulator import PolicySimulator
from tfrddlsim.viz import GenericVisualizer

# parameters
horizon = 40
batch_size = 32

# parse and compile RDDL
rddl = rddlgym.make('Reservoir-8', mode=rddlgym.AST)
compiler = Compiler(rddl, batch_size)
compiler.init()

# run simulations
policy = RandomPolicy(compiler)
simulator = PolicySimulator(compiler, policy)
trajectories = simulator.run(horizon)

# visualize trajectories
viz = GenericVisualizer(compiler, verbose=True)
viz.render(trajectories)

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).

Documentation

Please refer to https://tf-rddlsim.readthedocs.io/ for the code documentation.

Support

If you are having issues with tf-rddlsim, please let me know at: thiago.pbueno@gmail.com.

License

Copyright (c) 2018-2019 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/.

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