Prioritized Level Replay

This is a PyTorch implementation of Prioritized Level Replay.

Prioritized Level Replay is a simple method for improving generalization and sample-efficiency of deep RL agents on procedurally-generated environments by adaptively updating a sampling distribution over the training levels based on a score of the learning potential of replaying each level.

Requirements

conda create -n level-replay python=3.8
conda activate level-replay

git clone https://github.com/facebookresearch/level-replay.git
cd level-replay
pip install -r requirements.txt

# Clone a level-replay-compatible version of OpenAI Baselines.
git clone https://github.com/minqi/baselines.git
cd baselines 
python setup.py install
cd ..

# Clone level-replay-compatible versions of Procgen and MiniGrid environments.
git clone https://github.com/minqi/procgen.git
cd procgen 
python setup.py install
cd ..

git clone https://github.com/minqi/gym-minigrid .git
cd gym-minigrid 
pip install -e .
cd ..

Note that you may run into cmake finding an incompatible version of g++. You can manually specify the path to a compatible g++ by setting the path to the right compiler in procgen/procgen/CMakeLists.txt before the line project(codegen):

...
# Manually set the c++ compiler here
set(CMAKE_CXX_COMPILER "/share/apps/gcc-9.2.0/bin/g++")

project(codegen)
...

Examples

Train PPO with value-based level reply with rank prioritization on BigFish

python -m train --env_name bigfish \
--num_processes=64 \
--level_replay_strategy='value_l1' \
--level_replay_score_transform='rank' \
--level_replay_temperature=0.1 \
--staleness_coef=0.1

Procgen Benchmark results

Prioritized Level Replay results in statistically significant (★) improvements to generalization and sample-efficiency on most of the games in the Procgen Benchmark.

MiniGrid results

Likewise, Prioritized Level Replay results in drastic improvements to hard exploration environments in MiniGrid. On MiniGrid, we directly observe that the selective sampling employed by this method induces an implicit curriculum over levels from easier to harder levels.

Acknowledgements

The PPO implementation is largely based on Ilya Kostrikov's excellent implementation (https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail) and Roberta Raileanu's specific integration with Procgen (https://github.com/rraileanu/auto-drac).

Citation

If you make use of this code in your own work, please cite our paper:

@misc{jiang2020prioritized,
      title={{Prioritized Level Replay}}, 
      author={Minqi Jiang and Edward Grefenstette and Tim Rockt\"{a}schel},
      year={2020},
      eprint={2010.03934},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

License

The code in this repository is released under Creative Commons Attribution-NonCommercial 4.0 International License (CC-BY-NC 4.0).