Code for Go-Explore: a New Approach for Hard-Exploration Problems
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Paper located at:


Tested with Python 3.6. requirements.txt gives the exact libraries and versions used on a test machine able to run all phases. Unless otherwise specified, libraries can be installed using pip install <library_name>.

Required libraries for Phase 1:

  • matplotlib
  • loky==2.3.1
  • dataclasses
  • tqdm
  • gym
  • opencv-python

These libraries are sufficient to run Go-Explore Phase 1 with custom environments, which you may model after goexplore_py/ and goexplore_py/

The ALE/atari-py is not part of Go-Explore. If you are interested in running Go-Explore on Atari environments (for example to reproduce our experiments), you may install gym[atari] instead of just gym. Doing so will install atari-py. atari-py is licensed under GPLv2.

Additional libraries for demo generation:

  • ffmpeg (non-Python library, install using package manager)
  • imageio
  • fire

Additionally, to run gen_demo, you will need to clone openai/atari-demo and put a copy or link of the subfolder atari_demo at gen_demo/atari_demo in this codebase.

E.g. you could run:

git clone

cp -r atari-demo/atari_demo gen_demo

Additional libraries for Phase 2:

  • openmpi (non-Python library, install for source or using package manager)
  • tensorflow-gpu
  • pandas
  • horovod (install using HOROVOD_WITH_TENSORFLOW=1 pip install horovod
  • baselines (ignore mujoco-related errors)

Additionally, to run Phase 2, you will need to clone uber-research/atari-reset (note: this is an improved fork of the original project, which you can find at openai/atari-reset) and put it, copy it or link to it as atari_reset in the root folder for this project. E.g. you could run:

git clone atari_reset


Running Phase 1 of Go-Explore can be done using the script. To see the arguments for Phase 1, run:

./ --help

The default arguments for Phase 1 will run a domain knowledge version of Go-Explore Phase 1 on Montezuma's Revenge. However, the default parameters do not correspond to any experiment actually presented in the paper. To reproduce Phase 1 experiments from the paper, run one of ./, ./ or ./

Phase 1 produces a folder called results, and subfolders for each experiment, of the form 0000_fb6be589a3dc44c1b561336e04c6b4cb, where the first element is an automatically increasing experiment id and the second element is a random string that helps prevent race condition issues if two experiments are started at the same time and assigned the same id.

To generate demonstrations, call ./ <phase1_result_folder> <destination> --game <game>. Where <game> is one of "montezuma" (default) or "pitfall". The destination will be a directory containing a .demo file and a .mp4 file corresponding to the video of the demonstration.

To robustify (run Phase 2), put a set of .demo files from different runs of Phase 1 into a folder (we used 10 for Montezuma and 4 for Pitfall, a single demonstration can also work, but is less likely to succeed). Then run ./ <game> <demo_folder> <results_folder> where the game is one of MontezumaRevenge or Pitfall. This should work with mpirun if you are using distributed training (we used 16 GPUs). The indicator of success for Phase 2 is when one of the max_starting_point displayed in the log has reached a value near 0 (values less than around 80 are typically good). You may then test the performance of your trained neural network using ./ <game> <neural_net> <test_results_folder> where <neural_net> is one of the files produced by Phase 2 and printed in the log as Saving to .... This will produce .json files for each possible number of no-ops (from 0 to 30) with scores, levels and exact action sequences produced by the test runs.