A simple Jupyter notebook to visualize data in latent space using dimensionality reduction techniques. The notebook focuses on RL with image-based observations.
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)
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (TSNE)