rlmeta – a flexible lightweight research framework for Distributed
Reinforcement Learning based on PyTorch and


To build from source, please install PyTorch first,
and then run the commands below.

$ git clone https://github.com/facebookresearch/rlmeta
$ cd rlmeta
$ git submodule sync && git submodule update --init --recursive
$ pip install -e .

Run an Example

To run the example for Atari Pong game with PPO algorithm:

$ cd examples/atari/ppo
$ python atari_ppo.py env="PongNoFrameskip-v4" num_epochs=20

We are using hydra to define configs for trainining jobs.
The configs are defined in


The logs and checkpoints will be automatically saved to


After training, we can draw the training curve by run

$ python ../../plot.py --log_file=./outputs/{YYYY-mm-dd}/{HH:MM:SS}/atari_ppo.log --fig_file=./atari_ppo.png --xkey=time

One example of the training curve is shown below.



rlmeta is licensed under the MIT License. See LICENSE for details.


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