nikhilbarhate99 / Hierarchical Actor Critic Hac Pytorch
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PyTorch implementation of Hierarchical Actor Critic (HAC) for OpenAI gym environments
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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.pyfile. - 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)
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