This repository contains code for reproducing experiments from our paper "Hierarchical reinforcement learning with Timed Subgoals". The implementation of the Hierarchical reinforcement learning with Timed Subgoals (HiTS) algorithm can be found in the Graph-RL repository. A short video presentation summarizes the algorithm as well as our experiments.

HiTS enables sample-efficient learning in sparse-reward, long-horizong tasks. In particular, it extends subgoal-based hierarchical reinforcement learning to environments with dynamic elements which are, most of the time, beyond the control of the agent. Due to the use of timed subgoals and hindsight action relabeling the higher level sees transitions that are consistent with a stationary effective environment. As a result both levels in the hierarchy can learn concurrently and efficiently.

The three benchmark tasks in dynamic environments from the paper are contained in the dynamic-rl-benchmarks repository. If you are interested in applying HiTS to a different task, then this demo in the Graph-RL repository is the best place to start.

We recommend using a virtual environment with python3.7 or higher. Make sure pip is up to date. In the root directory of the repository execute:

pip install -r requirements.txt

To render episodes with one of the pretrained policies execute in the root directory:

python -m scripts.run.render --algo hits --env Platforms

Available algorithms:

• hits
• hac
• sac

Available environments:

• AntFourRooms
• Drawbridge
• Pendulum
• Platforms
• Tennis2D
• UR5Reacher

A policy can be be trained from scratch by running:

python -m scripts.run.train --algo hits --env Platforms

To render episodes with a newly trained policy use:

python -m scripts.run.render --algo hits --env Platforms --newly_trained

To render an episode with the stochastic policy used during training:

python -m scripts.run.render --algo hits --env Platforms --newly_trained --stochastic

Hyperparameters and seeds can be found in the graph_params.json files in the data directory. The key level_params_list contains a list of the hyperparameters of all levels, starting with the lowest level.

To enable tensoboard logs, add the key

"tensorboard_log": true

to the run_params.json file in the subdirectory of data corresponding to the desired environment. The tensorboard logs include returns, success rates, Q-values, number of actions used on each level before reaching the goal etc.

Some quantities like returns and success rates are also logged in CSV files stored in the data/*/log directories. To plot them to PDFs, run

python -m scripts.plot.plot_training data/Environment/algo_trained/

with the desired environment and algorithm. The output files are written to data/Environment/algo_trained/plots.

Note that the logged return corresponds to the cumulative reward up to the point when the episode ended, either because the goal was reached, the environment timed out or the action budget of the higher level was exhausted. If the action budget of the higher level is finite, the return is therefore not a good indicator for learning progress. If the reward is negative, then the return might be high at the beginning of training because the subgoal budget is exhausted quickly.

However, HiTS can be run without a finite subgoal budget. To run HiTS with this setting on Platforms, execute

python -m scripts.run.train --algo hits_no_budget --env Platforms

The logged returns will then be easier to interpret.

How HiTS outperforms baselines in dynamic environments can be seen in this video.

Please use the following BibTex entry.

@article{gurtler2021hierarchical,
  title={Hierarchical Reinforcement Learning with Timed Subgoals},
  author={G{\"u}rtler, Nico and B{\"u}chler, Dieter and Martius, Georg},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}