This is an implementation of the method proposed in RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments, which was published at ICLR 2020. The code includes all the baselines and ablations used in the paper.
We propose a novel type of intrinsic reward which encourges the agent to take actions that result in significant changes to its representation of the environment state.
# create a new conda environment
conda create -n ride-env python=3.6.8
conda activate ride-env
# install PyTorch
conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=9.0 -c pytorch
# install other requirements
git clone [email protected]:fairinternal/impact-driven-exploration.git
cd impact-driven-exploration
pip install -r requirements.txt
# install the MiniGrid environments
cd impact-driven-exploration
git clone https://github.com/maximecb/gym-minigrid.git
cd gym-minigrid
pip install -e .
cd impact-driven-exploration
OMP_NUM_THREADS=1 python main.py --model ride --env MiniGrid-MultiRoom-N12-S10-v0
OMP_NUM_THREADS=1 python main.py --model ride --env MiniGrid-MultiRoom-N10-S4-v0 --intrinsic_reward_coef 0.1 --entropy_cost 0.0005
Our vanilla RL algorithm is based on Torchbesat, which is an open source implementation of IMPALA.
This code is under the CC-BY-NC 4.0 (Attribution-NonCommercial 4.0 International) license.
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