Decoupling Exploration and Exploitation in Meta-Reinforcement Learning without Sacrifices

Introduction

Authors: Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn

Source code accompanying our ICML 2021 paper. Also see our project web page.

Requirements

This code requires Python3. The Python3 requirements are specified in requirements.txt. We recommend creating a virtualenv, e.g.:

virtualenv venv
source venv/bin/activate
pip install -r requirements.txt

Training

To train a meta-RL policy, invoke the following command:

python3 main.py exp_name -b environment=\"benchmark\"

This will create a directory experiments/exp_name, which will contain:

A tensorboard subdirectory at experiments/exp_name/tensorboard, which logs statistics, such as accumulated returns vs. number of training episodes, and also vs. number of training steps.
A visualization subdirectory at experiments/exp_name/visualize, which will contain videos of the learned agent.
A checkpoints subdirectory at experiments/exp_name/checkpoints, which will periodically save model checkpoints.
Metadata about the run, such as the configs used.

The benchmark argument specifies which of the benchmarks from the paper to use. The supported benchmarks are:

The sparse-reward 3D visual navigation benchmark: miniworld_sign
The cooking benchmark: cooking
The distracting bus benchmark: distraction
The map benchmark: map

Below, we provide the commands to reproduce the results from the paper. Each block of commands trains DREAM, E-RL^2, IMPORT, and VariBAD respectively, in the specified benchmark.

Sparse-Reward 3D Visual Navigation:

python3 main.py dream -b environment=\"miniworld_sign\" -c configs/default.json -c configs/miniworld.json
python3 main_varibad.py e-rl2 -b environment=\"miniworld_sign\" -c configs/rl2.json -c configs/rl2-miniworld.json
python3 main_varibad.py import -b environment=\"miniworld_sign\" -c configs/import.json -c configs/import-miniworld.json
python3 main_varibad.py varibad -b environment=\"miniworld_sign\" -c configs/varibad.json -c configs/varibad-miniworld.json

NOTE: Running MiniWorld headless typically requires xvfb-run. To do this, the NVIDIA GPU drivers must also be compiled with the --no-opengl-files flag. See the MiniWorld Troubleshooting Guide for more details. Commands for headless running below:

xvfb-run -a -s "-screen 0 1024x768x24 -ac +extension GLX +render -noreset" python3 main.py dream -b environment=\"miniworld_sign\" -c configs/default.json -c configs/miniworld.json
xvfb-run -a -s "-screen 0 1024x768x24 -ac +extension GLX +render -noreset" python3 main_varibad.py e-rl2 -b environment=\"miniworld_sign\" -c configs/rl2.json -c configs/rl2-miniworld.json
xvfb-run -a -s "-screen 0 1024x768x24 -ac +extension GLX +render -noreset" python3 main_varibad.py import -b environment=\"miniworld_sign\" -c configs/import.json -c configs/import-miniworld.json
xvfb-run -a -s "-screen 0 1024x768x24 -ac +extension GLX +render -noreset" python3 main_varibad.py varibad -b environment=\"miniworld_sign\" -c configs/varibad.json -c configs/varibad-miniworld.json

Distracting Bus:

python3 main.py dream -b environment=\"distraction\" -c configs/default.json
python3 main_varibad.py e-rl2 -b environment=\"distraction\" -c configs/rl2.json
python3 main_varibad.py import -b environment=\"distraction\" -c configs/import.json
python3 main_varibad.py varibad -b environment=\"distraction\" -c configs/varibad.json

Map:

python3 main.py dream -b environment=\"map\" -c configs/default.json
python3 main_varibad.py e-rl2 -b environment=\"map\" -c configs/rl2.json
python3 main_varibad.py import -b environment=\"map\" -c configs/import.json
python3 main_varibad.py varibad -b environment=\"map\" -c configs/varibad.json

Cooking:

python3 main.py dream -b environment=\"cooking\" -c configs/default.json
python3 main_varibad.py e-rl2 -b environment=\"cooking\" -c configs/rl2.json
python3 main_varibad.py import -b environment=\"cooking\" -c configs/import.json
python3 main_varibad.py varibad -b environment=\"cooking\" -c configs/varibad.json

Citation

If you use this code, please cite our paper.

@article{liu2020decoupling,
  title={Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices},
  author={Liu, Evan Zheran and Raghunathan, Aditi and Liang, Percy and Finn, Chelsea},
  journal={arXiv preprint arXiv:2008.02790},
  year={2020}
}

austrian-code-wizard / dream