TianhongDai / Hindsight Experience Replay
Licence: mit
This is the pytorch implementation of Hindsight Experience Replay (HER) - Experiment on all fetch robotic environments.
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Hindsight Experience Replay (HER)
This is a pytorch implementation of Hindsight Experience Replay.
Acknowledgement:
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
- [x] support GPU acceleration - although I have added GPU support, but I still not recommend if you don't have a powerful machine.
- [x] add multi-env per MPI.
- [x] 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).
- train the FetchReach-v1:
mpirun -np 1 python -u train.py --env-name='FetchReach-v1' --n-cycles=10 2>&1 | tee reach.log
- train the FetchPush-v1:
mpirun -np 8 python -u train.py --env-name='FetchPush-v1' 2>&1 | tee push.log
- train the FetchPickAndPlace-v1:
mpirun -np 16 python -u train.py --env-name='FetchPickAndPlace-v1' 2>&1 | tee pick.log
- 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 |
|---|---|---|
 |
 |
 |
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