[ICLR 2021] RAPID: A Simple Approach for Exploration in Reinforcement Learning

This is the Tensorflow implementation of ICLR 2021 paper Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments. We propose a simple method RAPID for exploration through scroring the previous episodes and reproducing the good exploration behaviors with imitation learning. overview

The implementation is based on OpenAI baselines. For all the experiments, add the option --disable_rapid to see the baseline result. RAPID can achieve better performance and sample efficiency than state-of-the-art exploration methods on MiniGrid environments. rendering performance

Cite This Work

title={Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments},
author={Daochen Zha and Wenye Ma and Lei Yuan and Xia Hu and Ji Liu},
booktitle={International Conference on Learning Representations},


Please make sure that you have Python 3.5+ installed. First, clone the repo with

git clone https://github.com/daochenzha/rapid.git
cd rapid

Then install the dependencies with pip:

pip install -r requirements.txt
pip install -e .

To run MuJoCo experiments, you need to have the MuJoCo license. Install mujoco-py with

pip install mujoco-py==

How to run the code

The entry is main.py. Some important hyperparameters are as follows.

  • --env: what environment to be used
  • --num_timesteps: the number of timesteps to be run
  • --w0: the weight of extrinsic reward score
  • --w1: the weight of local score
  • --w2: the weight of global score
  • --sl_until: do the RAPID update until which timestep
  • --disable_rapid: use it to compare with PPO baseline
  • --log_dir: the directory to save logs

Reproducing the result of MiniGrid environments

For MiniGrid-KeyCorridorS3R2, run

python main.py --env MiniGrid-KeyCorridorS3R2-v0 --sl_until 1200000

For MiniGrid-KeyCorridorS3R3, run

python main.py --env MiniGrid-KeyCorridorS3R3-v0 --sl_until 3000000

For other environments, run

python main.py --env $ENV

where $ENV is the environment name.

Run MiniWorld Maze environment

  1. Clone the latest master branch of MiniWorld and install it
git clone -b master --single-branch --depth=1 https://github.com/maximecb/gym-miniworld.git
cd gym-miniwolrd
pip install -e .
cd ..
  1. Start training with
python main.py --env MiniWorld-MazeS5-v0 --num_timesteps 5000000 --nsteps 512 --w1 0.00001 --w2 0.0 --log_dir results/MiniWorld-MazeS5-v0

For server without screens, you may install xvfb with

apt-get install xvfb

Then start training with

xvfb-run -a -s "-screen 0 1024x768x24 -ac +extension GLX +render -noreset" python main.py --env MiniWorld-MazeS5-v0 --num_timesteps 5000000 --nsteps 512 --w1 0.00001 --w2 0.0 --log_dir results/MiniWorld-MazeS5-v0

Run MuJoCo experiments


python main.py --seed 0 --env $env --num_timesteps 5000000 --lr 5e-4 --w1 0.001 --w2 0.0 --log_dir logs/$ENV/rapid

where $ENV can be EpisodeSwimmer-v2, EpisodeHopper-v2, EpisodeWalker2d-v2, EpisodeInvertedPendulum-v2, DensityEpisodeSwimmer-v2, or ViscosityEpisodeSwimmer-v2.