What is latent space planning in RL?

Latent space planning in reinforcement learning (RL) refers to methods that perform decision-making using a compressed, abstract representation (latent space) of the environment. Instead of operating directly on raw observations like pixels or sensor data, the agent learns a lower-dimensional encoding that captures essential features of the state. This approach simplifies planning by reducing computational complexity, enabling the agent to explore possible future trajectories more efficiently. For example, in a robotics task, raw camera images might be compressed into a latent representation that encodes object positions and movements, allowing the agent to plan actions without processing high-dimensional visual data at every step.

A key advantage of latent space planning is its integration with model-based RL. Here, the agent learns a dynamics model that predicts how the latent state evolves over time based on actions. By simulating trajectories in this compact space, the agent can evaluate potential action sequences faster than in the original state space. For instance, algorithms like Dreamer or PlaNet use neural networks to predict future latent states and rewards, enabling planning through techniques like tree search or gradient-based optimization. This reduces the need for exhaustive trial-and-error in the real environment, which is especially useful in settings where data collection is slow or costly, such as real-world robotics or complex simulations.

Practical applications of latent space planning often involve balancing abstraction with accuracy. For example, a self-driving car agent might use a latent model to predict traffic patterns based on encoded representations of camera and lidar data, ignoring irrelevant details like weather effects. However, a challenge is ensuring the latent space retains enough information for reliable predictions. Poorly designed encoders might discard critical features, leading to flawed plans. To address this, methods like variational autoencoders (VAEs) or contrastive learning are used to train encoders that preserve task-relevant information. By combining efficient planning with learned representations, latent space methods enable RL agents to scale to complex environments while remaining computationally tractable.