HERO

Code for Combining Hindsight and Imagination in Multi-goal ReinforcementLearning。

Installation

• Install the requirements such as tensorflow, mpi4py, gym, and mujoco_py using pip;

• Clone the repo and cd into it;

• Install baselines package

  pip install -e .

Usage

Experiment environments: FetchReach-v1, FetchPush-v1, FetchSlide-v1, FetchPickAndPlace-v1, HandReach-v0, HandManipulateBlockRotateXYZ-v0.

DDPG:

python -m  baselines.run  --env=FetchPush-v1 --num_epoch 50 --num_env 12 --noher True --log_path=~/logs/FetchPush_env12/ --save_path=~/ddpg/fetchpush/

HER:

python -m  baselines.run  --env=FetchPush-v1 --num_epoch 50 --num_env 12 --log_path=~/logs/FetchPush_env12/ --save_path=~/her/fetchpush/

HERO:

python -m  baselines.run --env=FetchPush-v1 --num_epoch 50 --num_env 12  --n_step 2 --mode dynamic --alpha 0.4 --log_path=~/logs/FetchPush_env12_nstep_2/ --save_path=~/policies/mmher/fetchpush/

Main Functions

The main functions of our algorithms are in /baselines/her/her_sampler.py. Names of our main function is _sample_nstep_dynamic_her_transitions.