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Matthias Plappert
2018-03-26 16:50:22 +02:00
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# Hindsight Experience Replay
For details on Hindsight Experience Replay (HER), please read the [paper](https://arxiv.org/pdf/1707.01495.pdf).
For details on Hindsight Experience Replay (HER), please read the [paper](https://arxiv.org/abs/1707.01495).
## How to use Hindsight Experience Replay
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This should visualize the current policy for 10 episodes and will also print statistics.
### Advanced usage
The train script comes with advanced features like MPI support, that allows to scale across all cores of a single machine.
To see all available options, simply run this command:
### Reproducing results
In order to reproduce the results from [Plappert et al. (2018)](https://arxiv.org/abs/1802.09464), run the following command:
```bash
python -m baselines.her.experiment.train --help
python -m baselines.her.experiment.train --num_cpu 19
```
To run on, say, 20 CPU cores, you can use the following command:
```bash
python -m baselines.her.experiment.train --num_cpu 20
```
That's it, you are now running rollouts using 20 MPI workers and average gradients for network updates across all 20 core.
This will require a machine with sufficient amount of physical CPU cores. In our experiments,
we used [Azure's D15v2 instances](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/sizes),
which have 20 physical cores. We only scheduled the experiment on 19 of those to leave some head-room on the system.