christofer-f / hierarchical-actor-critic-hac-pytorch

PyTorch implementation of Hierarchical Actor Critic (HAC) for OpenAI gym environments

This is a modification of the HAC algorithm implementing the go-explore algorithm... sort of... anyway it seems to work really well

The code is not optimized but works well for toy problems

Serie1: Standard exploration

Serie2: GO-EXPLORE is active (https://github.com/uber-research/go-explore)

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

To render the environments (Mountain Car and Pendulum) with subgoals (2 or 3 level) replace the gym files in local installation directory gym/envs/classic_control with the files in gym folder of this repo and change the bool render to True

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.

Requirements

• Python 3.6
• PyTorch
• OpenAI gym

Results

MountainCarContinuous-v0

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

References

• Official Paper and Code (TensorFlow)