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Training agents to plan in latent space — a technical overview | by Lukas Bierling | Medium

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Reinforcement Learning (RL) revolves around an agent navigating an environment, making decisions, and learning to maximize long-term rewards. At its core, this process is modeled through Markov Decision Processes (MDPs), a framework that has driven significant advancements in AI. However, traditional RL methods often struggle with sample inefficiency and high computational costs, particularly in complex, uncertain environments. This article explores a novel approach that extends Model-Based Reinforcement Learning (MBRL). Instead of constructing models directly from raw observations, this method operates in a compact, encoded latent space. By using“imagination” within this latent representation, agents can simulate possible futures, refine their strategies, and plan more effectively — mitigating many of the challenges faced by standard RL techniques. At the heart of this approach lies the Recurrent State Space Model (RSSM), a powerful framework designed to capture and predict environmen

Photo by Milad Fakurian on Unsplash Training agents to plan in latent space — a technical overview How can we design sample efficient agents in complex environments without perfect environment models? Lukas Bierling 20 min read · Jan 2, 2025 -- Listen Share Introduction Reinforcement Learning (RL) revolves around an agent navigating an environment, making decisions, and learning to maximize long-term rewards. At its core, this process is modeled through Markov Decision Processes (MDPs), a framework that has driven significant advancements in AI. However, traditional RL methods often struggle w

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