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Found 6 papers, 1 papers with code
Guiding Representation Learning in Deep Generative Models with Policy Gradients
Therefore, this paper investigates the issues of joint training approaches and explores incorporation of policy gradients from RL into the VAE's latent space to find a task-specific latent space representation.
Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod Robot
Locomotion is a prime example for adaptive behavior in animals and biological control principles have inspired control architectures for legged robots.
A Perceived Environment Design using a Multi-Modal Variational Autoencoder for learning Active-Sensing
This contribution comprises the interplay between a multi-modal variational autoencoder and an environment to a perceived environment, on which an agent can act.
Deep Generative Models for learning Coherent Latent Representations from Multi-Modal Data
The application of multi-modal generative models by means of a Variational Auto Encoder (VAE) is an upcoming research topic for sensor fusion and bi-directional modality exchange.
M$^2$VAE - Derivation of a Multi-Modal Variational Autoencoder Objective from the Marginal Joint Log-Likelihood
This work gives an in-depth derivation of the trainable evidence lower bound obtained from the marginal joint log-Likelihood with the goal of training a Multi-Modal Variational Autoencoder (M$^2$VAE).
Coordinated Heterogeneous Distributed Perception based on Latent Space Representation
We investigate a reinforcement approach for distributed sensing based on the latent space derived from multi-modal deep generative models.
