tf-plan 0.5.1

Planning through backpropagation using TensorFlow.

tf-plan

Planning via gradient-based optimization in continuous MDPs using TensorFlow.

tf-plan is an implementation based on the NIPS 2017 paper:

Wu Ga, Buser Say, and Scott Sanner, 2017
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains.
In Advances in Neural Information Processing Systems (pp. 6273-6283).

Quickstart

tf-plan is a Python3.5+ package available in PyPI.

$ pip3 install tf-plan

Features

tf-plan solves discrete time MDPs with continuous state-action spaces and deterministic transitions.

The domains/instances are specified using the RDDL language.

It is built on Python3's RDDL toolkit:

• pyrddl: RDDL lexer/parser in Python3.
• rddlgym: A toolkit for working with RDDL domains in Python3.
• rddl2tf: RDDL2TensorFlow compiler.
• tf-rddlsim: A RDDL simulator running in TensorFlow.

Please refer to the projects' documentation for further details.

Usage

$ tfplan --help

usage: tfplan [-h] [-m {offline,online}] [-b BATCH_SIZE] [-hr HORIZON]
              [-e EPOCHS] [-lr LEARNING_RATE] [--viz {generic,navigation}]
              [-v]
              rddl

tf-plan (v0.5.0): Planning via gradient-based optimization in TensorFlow.

positional arguments:
  rddl                  RDDL file or rddlgym domain id

optional arguments:
  -h, --help            show this help message and exit
  -m {offline,online}, --mode {offline,online}
                        planning mode (default=offline)
  -b BATCH_SIZE, --batch-size BATCH_SIZE
                        number of trajectories in a batch (default=128)
  -hr HORIZON, --horizon HORIZON
                        number of timesteps (default=40)
  -e EPOCHS, --epochs EPOCHS
                        number of timesteps (default=500)
  -lr LEARNING_RATE, --learning-rate LEARNING_RATE
                        optimizer learning rate (default=0.001)
  --viz {generic,navigation}
                        type of visualizer (default=generic)
  -v, --verbose         verbosity mode

Examples

Navigation

$ tfplan Navigation-v1 -b 32 -hr 15 -e 1000 -v --viz=navigation

Running tf-plan v0.5.0 ...
>> RDDL:            Navigation-v1
>> Planning mode:   offline
>> Horizon:         15
>> Batch size:      32
>> Training epochs: 1000
>> Learning rate:   0.01

Epoch   999: loss = 6879.5073244
>> total reward = -82.927887

HVAC

$ tfplan HVAC-V1 -b 64 -hr 40 -e 1000 --viz=generic

Epoch   999: loss = 58134777856.00000000
>> total reward = -241098.296875

Documentation

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

Support

If you are having issues with tf-plan, please let me know at: [e-mail].

License

Copyright (c) 2018-2019 Thiago Pereira Bueno All Rights Reserved.

tf-plan 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-plan 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-plan. If not, see http://www.gnu.org/licenses/.