her

Hindsight Experience Replay

For details on Hindsight Experience Replay (HER), please read the paper.

How to use Hindsight Experience Replay

Getting started

Training an agent is very simple:

python -m baselines.her.experiment.train

This will train a DDPG+HER agent on the FetchReach environment. You should see the success rate go up quickly to 1.0, which means that the agent achieves the desired goal in 100% of the cases. The training script logs other diagnostics as well and pickles the best policy so far (w.r.t. to its test success rate), the latest policy, and, if enabled, a history of policies every K epochs.

To inspect what the agent has learned, use the play script:

python -m baselines.her.experiment.play /path/to/an/experiment/policy_best.pkl

You can try it right now with the results of the training step (the script prints out the path for you). This should visualize the current policy for 10 episodes and will also print statistics.

Reproducing results

In order to reproduce the results from Plappert et al. (2018), run the following command:

python -m baselines.her.experiment.train --num_cpu 19

This will require a machine with sufficient amount of physical CPU cores. In our experiments, we used Azure's D15v2 instances, which have 20 physical cores. We only scheduled the experiment on 19 of those to leave some head-room on the system.