#!/bin/bash
       
 
git clone https://github.com/erdiphd/HER_force.git
cd HER_force/docker

#train our methods
docker-compose run --rm  -e mujoco_env=FetchPickAndPlace-v1 -e log_tag=log/t1_contact_energy_pick  -e n_epochs=50  -e num_cpu=20 -e  prioritization=contact_energy -e reward_type=sparse her_tactile
docker-compose run --rm  -e mujoco_env=FetchPush-v1 -e log_tag=log/t1_contact_energy_push  -e n_epochs=50  -e num_cpu=20 -e  prioritization=contact_energy -e reward_type=sparse her_tactile
docker-compose run --rm  -e mujoco_env=FetchSlide-v1 -e log_tag=log/t1_contact_energy_slide  -e n_epochs=50  -e num_cpu=20 -e  prioritization=contact_energy -e reward_type=sparse her_tactile

#train cper methods
docker-compose run --rm  -e mujoco_env=FetchPickAndPlace-v1 -e log_tag=log/t1_cper_pick  -e n_epochs=50  -e num_cpu=20 -e  prioritization=cper -e reward_type=intrinsic her_tactile
docker-compose run --rm  -e mujoco_env=FetchPush-v1 -e log_tag=log/t1_cper_push  -e n_epochs=50  -e num_cpu=20 -e  prioritization=cper -e reward_type=intrinsic her_tactile
docker-compose run --rm -e mujoco_env=FetchSlide-v1 -e log_tag=log/t1_cper_slide  -e n_epochs=50  -e num_cpu=20 -e  prioritization=cper -e reward_type=intrinsic her_tactile

#train MEP methods
docker-compose run --rm  -e mujoco_env=FetchPickAndPlace-v1 -e log_tag=log/t1_mep_pick  -e n_epochs=50  -e num_cpu=20 -e  prioritization=entropy -e reward_type=sparse her_tactile
docker-compose run --rm  -e mujoco_env=FetchPush-v1 -e log_tag=log/t1_mep_push  -e n_epochs=50  -e num_cpu=20 -e  prioritization=entropy -e reward_type=sparse her_tactile
docker-compose run --rm  -e mujoco_env=FetchSlide-v1 -e log_tag=log/t1_mep_slide  -e n_epochs=50  -e num_cpu=20 -e  prioritization=entropy -e reward_type=sparse her_tactile

#train PER methods
docker-compose run --rm  -e mujoco_env=FetchPickAndPlace-v1 -e log_tag=log/t1_per_pick  -e n_epochs=50  -e num_cpu=20 -e  prioritization=tderror -e reward_type=sparse her_tactile
docker-compose run --rm  -e mujoco_env=FetchPush-v1 -e log_tag=log/t1_per_push  -e n_epochs=50  -e num_cpu=20 -e  prioritization=tderror -e reward_type=sparse her_tactile
docker-compose run --rm  -e mujoco_env=FetchSlide-v1 -e log_tag=log/t1_per_slide  -e n_epochs=50  -e num_cpu=20 -e  prioritization=tderror -e reward_type=sparse her_tactile

#train her methods
docker-compose run --rm  -e mujoco_env=FetchPickAndPlace-v1 -e log_tag=log/t1_her_pick  -e n_epochs=50  -e num_cpu=20 -e  prioritization=none -e reward_type=sparse her_tactile
docker-compose run --rm  -e mujoco_env=FetchPush-v1 -e log_tag=log/t1_her_push  -e n_epochs=50  -e num_cpu=20 -e  prioritization=none -e reward_type=sparse her_tactile
docker-compose run --rm  -e mujoco_env=FetchSlide-v1 -e log_tag=log/t1_her_slide  -e n_epochs=50  -e num_cpu=20 -e  prioritization=none -e reward_type=sparse her_tactile

#train EBP methods
git clone https://github.com/ruizhaogit/EnergyBasedPrioritization
#Please use our Gym environment with the EBP

  

Creating virtual anaconda python environment

#!/bin/bash
 
source /home/user/conda/bin/activate
conda create --name her python=3.7 -y
conda activate her
pip install numpy && pip install cffi lockfile imageio glfw tensorflow==1.14 cython && pip install mujoco_py && pip install beautifultable==0.7.0
pip install numpy-quaternion gym==0.15.4 click joblib mpi4py scipy  protobuf==3.19 scikit-learn pyyaml pyquaternion
    
#Clone the repository
    
git clone https://github.com/erdiphd/HER_force.git
    
#train the robot
    
cd HER_force/code/Algorithm
conda activate her
python baselines/her/experiment/train.py --env_name  FetchPickAndPlace-v1 --logdir=log/t1_contact_energy  --n_epochs=50 --num_cpu=20 --prioritization=contact_energy --reward_type sparse