mengf1/hindsight-experience-replay

This is the pytorch implementation of Hindsight Experience Replay (HER) - Experiment on all fetch robotic environments.

Hindsight Experience Replay (HER)

This is a pytorch implementation of Hindsight Experience Replay.

Acknowledgement:

• Openai Baselines

Requirements

• python=3.5.2
• openai-gym=0.12.5 (mujoco200 is supported, but you need to use gym >= 0.12.5, it has a bug in the previous version.)
• mujoco-py=1.50.1.56 (Please use this version, if you use mujoco200, you may failed in the FetchSlide-v1)
• pytorch=1.0.0 (If you use pytorch-0.4.1, you may have data type errors. I will fix it later.)
• mpi4py

TODO List

• support GPU acceleration - although I have added GPU support, but I still not recommend if you don't have a powerful machine.
• add multi-env per MPI.
• add the plot and demo of the FetchSlide-v1.

Instruction to run the code

If you want to use GPU, just add the flag --cuda (Not Recommended, Better Use CPU).

1. train the FetchReach-v1:

mpirun -np 1 python -u train.py --env-name='FetchReach-v1' --n-cycles=10 2>&1 | tee reach.log

2. train the FetchPush-v1:

mpirun -np 8 python -u train.py --env-name='FetchPush-v1' 2>&1 | tee push.log

3. train the FetchPickAndPlace-v1:

mpirun -np 16 python -u train.py --env-name='FetchPickAndPlace-v1' 2>&1 | tee pick.log

4. train the FetchSlide-v1:

mpirun -np 8 python -u train.py --env-name='FetchSlide-v1' --n-epochs=200 2>&1 | tee slide.log

Play Demo

python demo.py --env-name=<environment name>

Download the Pre-trained Model

Please download them from the Google Driver, then put the saved_models under the current folder.

Results

Training Performance

It was plotted by using 5 different seeds, the solid line is the median value.

Demo:

Tips: when you watch the demo, you can press TAB to switch the camera in the mujoco.

FetchPush-v1	FetchPickAndPlace-v1	FetchSlide-v1