Welcome to State Representation Learning Zoo’s documentation!¶
A collection of State Representation Learning (SRL) methods for Reinforcement Learning, written using PyTorch.
Github repo: https://github.com/araffin/srl-zoo
Note
This repo is part of the S-RL Toolbox
Available methods:
- SRL with Robotic Priors + extensions (stereovision, additional priors)
- Denoising Autoencoder (DAE)
- Variational Autoencoder (VAE) and beta-VAE
- PCA
- Supervised Learning
- Forward, Inverse Models
- Triplet Network (for stereovision only)
- Reward loss
- Combination and stacking of methods
- Random Features
- [experimental] Reward Prior, Episode-prior, Perceptual Similarity loss (DARLA), Mutual Information loss
Related papers:
- “S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning” (Raffin et al., 2018) https://arxiv.org/abs/1809.09369
- “State Representation Learning for Control: An Overview” (Lesort et al., 2018), link: https://arxiv.org/pdf/1802.04181.pdf
- “Learning State Representations with Robotic Priors” (Jonschkowski and Brock, 2015), link: http://tinyurl.com/gly9sma