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BlockWorldRoboticAgent Terminate properly Oct 27, 2017
BlockWorldSimulator
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README.md

README.md

Blocks World -- Simulator, Code, and Models (Misra et al. EMNLP 2017)

(This is a developmental code for early release. A stable release will go out soon. Please contact dkm@cs.cornell.edu for more details.)

Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
Dipendra Misra, John Langford, and Yoav Artzi
In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017.

simulation

The original environment was designed by Bisk et al. 2016, who also collected the data.

Run the Code in 60 Seconds (Requires only python 2.7)

In this section, we will run the oracle baseline on the devset. This will give an idea of the simulator and the code and does not requires any dependency besides python 2.7.

Supports: Linux (with Unity Desktop) (Mac build to be supported soon)

Requires: python2.7

Run a simple baseline

  1. Clone the code git clone https://github.com/clic-lab/blocks

  2. Go to ./blocks/BlockWorldSimulator/ and make the file linux_build.x86_64 executable by running:

    chmod 777 linux_build.x86_64

  3. Now run the file linux_build.x86_64 by double clicking.

    Choose the Fastest mode setting and any resolution (does not matter which resolution).

    Note: The screen will remain black and the window will be frozen until you run the python agent.

  4. Finally run the oracle baseline by running the following command in the home directory. May take 5-10 seconds for the simulator to be ready and before following command works.

    cd ./blocks/BlockWorldRoboticAgent/

    export PYTHONPATH=<location-of-blocks-folder>/BLockWorldRoboticAgent/:$PYTHONPATH

    python ./experiments/test_oracle.py

    You can similarly run python ./experiments/test_stop.py and python ./experiments/test_random.py to run stop and random walk baselines respectively.

  5. The log will be generated in ./blocks/BlockWorldRoboticAgent/log.txt and final number should match the numbers in the paper 0.35 mean distance error.

Instructions for running other baselines will come soon.


Production Release and pre-trained models coming soon ...

Attribution

@InProceedings{D17-1107,
  author = 	"Misra, Dipendra
		and Langford, John
		and Artzi, Yoav",
  title = 	"Mapping Instructions and Visual Observations to Actions with Reinforcement Learning",
  booktitle = 	"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
  year = 	"2017",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"1015--1026",
  location = 	"Copenhagen, Denmark",
  url = 	"http://aclweb.org/anthology/D17-1107"
}