Latent Space Plots
A simple Jupyter notebook to visualize data in latent space using dimensionality reduction techniques. The notebook focuses on RL with image-based observations.
Data basis
Latent vectors and rewards: (n, k + 1)
| Index | Feature_0 | Feature_1 | Feature_2 | Feature_k | Reward |
|---|---|---|---|---|---|
| 0 | ... | ... | ... | ... | ... |
| 1 | ... | ... | ... | ... | ... |
| 2 | ... | ... | ... | ... | ... |
| n | ... | ... | ... | ... | ... |
Corresponding images: (n, 3, 64, 64)
Dimensionality reduction options
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (TSNE)
Visualizations
2D plot
3D plot
3D animation
2D plot with images
3D plot with images
3D animation with images
