Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay
This is the official implementation of our paper "Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay" (PRICAI 2021).
Pre-requisites
- python==3.6.8
- gym==0.12.5
- mujoco-py==2.0.2.0
- tensorflow==1.13.1
Installation
- Install the OpenMPI:
sudo apt-get install openmpi-bin openmpi-doc libopenmpi-dev
- Install the required python packages.
- Install
baseline
:
pip install -e .
Usage
Enter to the specific location:
cd ./baselines/her/experiment
The scripts for FetchEnv:
python train.py --env_name FetchPush-v1 --clip_div=0.001 --use_kdpp True --prioritization diversity --seed 0 --n_epochs 50 --num_cpu 16 --logdir logs/fetchpushv1/cpu16ep50/alg=DDPG+DTGSH=/r0 --logging True
python train.py --env_name FetchPickAndPlace-v1 --clip_div=0.001 --use_kdpp True --prioritization diversity --seed 0 --n_epochs 50 --num_cpu 16 --logdir logs/fetchpickv1/cpu16ep50/alg=DDPG+DTGSH=/r0 --logging True
The scripts for HandEnv:
python train.py --env_name HandManipulateEggFull-v0 --use_kdpp True --prioritization diversity --seed 0 --goal_type full --sigma 0.1 --n_epochs 50 --num_cpu 16 --logdir logs/handegg1/cpu16ep50/alg=DDPG+DTGSH=/r0 --logging True
python train.py --env_name HandManipulateBlockRotateXYZ-v0 --use_kdpp True --prioritization diversity --seed 0 --goal_type rotate --sigma 0.1 --n_epochs 50 --num_cpu 16 --logdir logs/handblock1/cpu16ep50/alg=DDPG+DTGSH=/r0 --logging True
python train.py --env_name HandManipulatePenRotate-v0 --use_kdpp True --prioritization diversity --seed 0 --goal_type rotate --sigma 0.1 --n_epochs 50 --num_cpu 16 --logdir logs/handpen1/cpu16ep50/alg=DDPG+DTGSH=/r0 --logging True