Current foundations

Architectural Foundations: Building the Visual Agent Mind's Eye

Enabling visual imagination and dreams requires specialized architectures:

• Advanced Visual Generative Models: The core engine for synthesizing pixels.

  • Diffusion Models: State-of-the-art for high-fidelity, controllable image and video synthesis (e.g., Stable Diffusion, Sora). Enable precise conditioning for goal-directed visualization.

  • Generative Adversarial Networks (GANs): Generate realistic novel visuals through adversarial training.

  • Variational Autoencoders (VAEs): Learn compressed latent spaces allowing efficient sampling and manipulation for novel visual concept generation and variation.

  • *Neural Radiance Fields (NeRFs) & 3D-Aware Models:* Construct implicit 3D representations from 2D images, enabling viewpoint synthesis, novel scene generation, and lighting manipulation – crucial for spatial imagination.

• Visual World Models: Go beyond state vectors to predict future sensory states, particularly visual frames. These are often recurrent neural networks or transformer-based models trained to predict the next frame(s) given past frames and actions. Accuracy is paramount for reliable imagination (e.g., DreamerV3's latent dynamics model).

• Visual Planning & Search: Algorithms (MCTS, MPC) utilize the visual world model to simulate action sequences and predict their visual outcomes. Agents evaluate potential actions based on these imagined visual futures.

• Reinforcement Learning with Visual Focus: Off-policy RL algorithms combined with:

  • Visual Experience Replay: Storing and replaying frames or latent representations.

  • Model-Based Visual Rollouts: Using the visual world model for extensive offline policy training via simulated visual trajectories ("dream training").

• Visual Memory Systems: Episodic memory storing key visual experiences or compressed latent representations for later replay or recombination during dreaming/imagination.