Jun 8, 2021 · We present a new approach that handles stochastic and partially-observable environments. Our key insight is to use discrete autoencoders to capture the ...
Values of leaf nodes are used to estimate the Q-values of all the actions in the root node.
It is free to represent state in whatever way is relevant to planning, with no other constraint. This is the so-called Value Equivalence principle.
Vector Quantized Models for Planning. Additional material for Vector Quantized Models for Planning. Accepted at ICML 2021, arXiv.
Recent developments in the field of model-based RL have proven successful in a range of environments, especially ones where planning is essential.
Duration: 51:11
Posted: Oct 22, 2023
Posted: Oct 22, 2023
Jun 8, 2021 · Planning in Stochastic Environments with a Learned Model · Hierarchical Imitation Learning with Vector Quantized Models · Transformers are Sample ...
Aug 28, 2024 · The Vector Quantized Variational Autoencoder (VQ-VAE) leverages a unique mechanism called vector quantization to map continuous latent representations into ...
Nov 4, 2024 · Vector Quantization (VQ) is a widely used method for converting continuous representations into discrete codes, which has become fundamental in ...
Missing: Planning. | Show results with:Planning.
Sep 25, 2024 · Vector quantization is a data compression technique used to reduce the size of high-dimensional data. Compressing vectors reduces memory usage.