Answers.Body: <p>If you're primarily concerned with computational efficiency and simplifying training while still achieving decent performance, VQ-VAE is a good candidate for model-based RL. The common blurriness in the VAE's reconstructions may not be a major issue if your task doesn't require high-quality visual feedback but rather focuses on the world dynamics and sequential decision-making. To fully leverage the additional sequential data input in VQ-VAE, you can <em>concatenate</em> all past actions or previous states or both with the current visual data input to pass through the VQ-VAE encoder. By providing the additional sequential context, the encoder can learn latent representations that are influenced by the temporal history to be capable for modeling sequential dependencies in the environment.</p>
  <p>If your task requires <em>disentangled</em> latent variables such as separating the agent's position, velocity, and object state, models like Beta-VAE might be helpful.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-12-17T08:21:58.200Z
Answers.LastActivityDate: 2024-12-17T08:21:58.200Z
Answers.UserDisplayName: cinch
Body: <p>The DreamerV3 (world models-based) architecture uses VQ-VAE to process visual data from the agent and teaches world dynamics through the latent space. I guess the primary is motivation behind this to reduce unnecessary data, compress the input size, and simplify training.
  In the paper, I noticed that the images sampled from the VQ-VAE are blurry and not very sequentially consistent. While I understand that the blurriness might be due to the variational nature of the model but I don't know whether this architecture is of sufficient quality for model-based RL and whether I should go for another model.</p>
  <p>For example, when I look for such a latent space learner, I come across latent diffusion models or beta-vae for distangle. However, since they are expensive to train, I can't decide whether they are suitable for RL.</p>
  <p>I noticed that in DreamerV3, sequential data is provided as an additional input to the encoder. Maybe I should choose a latent space learner that can handle sequence data. I'm very confused, and I’m not sure whether I should continue with VQ-VAE or look for a better architecture.</p>
  <p><strong>In short how can we make the best decisions when selecting a latent space learner for our reinforcement learning algorithm to achieve optimal results?</strong></p>
  
Tags: <reinforcement-learning><machine-learning><autoencoders><variational-autoencoder><latent-variable>
Title: How should we choose a latent space learner for reinforcement learning?
UserDisplayName: Kerem
ThreadText: # How should we choose a latent space learner for reinforcement learning?
  
  (Asked by: username_0 on 2024-12-15)
  
  The DreamerV3 (world models-based) architecture uses VQ-VAE to process visual data from the agent and teaches world dynamics through the latent space. I guess the primary is motivation behind this to reduce unnecessary data, compress the input size, and simplify training.
  In the paper, I noticed that the images sampled from the VQ-VAE are blurry and not very sequentially consistent. While I understand that the blurriness might be due to the variational nature of the model but I don't know whether this architecture is of sufficient quality for model-based RL and whether I should go for another model.
  
  For example, when I look for such a latent space learner, I come across latent diffusion models or beta-vae for distangle. However, since they are expensive to train, I can't decide whether they are suitable for RL.
  
  I noticed that in DreamerV3, sequential data is provided as an additional input to the encoder. Maybe I should choose a latent space learner that can handle sequence data. I'm very confused, and I’m not sure whether I should continue with VQ-VAE or look for a better architecture.
  
  **In short how can we make the best decisions when selecting a latent space learner for our reinforcement learning algorithm to achieve optimal results?**
  
  **Tags:** reinforcement-learning, machine-learning, autoencoders, variational-autoencoder, latent-variable
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-12-17. Score: 0 | Accepted answer
  
  If you're primarily concerned with computational efficiency and simplifying training while still achieving decent performance, VQ-VAE is a good candidate for model-based RL. The common blurriness in the VAE's reconstructions may not be a major issue if your task doesn't require high-quality visual feedback but rather focuses on the world dynamics and sequential decision-making. To fully leverage the additional sequential data input in VQ-VAE, you can *concatenate* all past actions or previous states or both with the current visual data input to pass through the VQ-VAE encoder. By providing the additional sequential context, the encoder can learn latent representations that are influenced by the temporal history to be capable for modeling sequential dependencies in the environment.
  
  If your task requires *disentangled* latent variables such as separating the agent's position, velocity, and object state, models like Beta-VAE might be helpful.
  
  
  ---
  
  