Hierarchical-Actor-Critic-HAC-PyTorch

This is an implementation of the Hierarchical Actor Critic (HAC) algorithm described in the paper, Learning Multi-Level Hierarchies with Hindsight (ICLR 2019), in PyTorch for OpenAI gym environments. The algorithm learns to reach a goal state by dividing the task into short horizon intermediate goals (subgoals).

Usage

• All the hyperparameters are contained in the train.py file.
• To train a new network run train.py
• To test a preTrained network run test.py
• For a detailed explanation of offsets and bounds, refer to issue #2
• For hyperparameters used for preTraining the pendulum policy refer to issue #3

Implementation Details

• The code is implemented as described in the appendix section of the paper and the Official repository, i.e. without target networks and with bounded Q-values.
• The Actor and Critic networks have 2 hidded layers of size 64.

Citing

Please use this bibtex if you want to cite this repository in your publications :

@misc{pytorch_hac,
    author = {Barhate, Nikhil},
    title = {PyTorch Implementation of Hierarchical Actor-Critic},
    year = {2021},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/nikhilbarhate99/Hierarchical-Actor-Critic-HAC-PyTorch}},
}

Requirements

• Python 3.6
• PyTorch
• OpenAI gym

Results

MountainCarContinuous-v0

(2 levels, H = 20, 200 episodes)	(3 levels, H = 5, 200 episodes)

(2 levels, H = 20, 200 episodes)

References

• Official Paper and Code (TensorFlow)