In the pursuit of artificial general intelligence, reinforcement learning (RL) agents often stumble on a fundamental hurdle: generalization. Train a Proximal Policy Optimization (PPO) or Deep Q-Network (DQN) agent on a single grid-based navigation layout, and it masters that specific configuration. Shift a key, add a wall, or alter a passage, and performance collapses. The agent didn't learn the rules of the game—it memorized the geometry. This memorization problem plagues even hierarchical RL methods, which are theoretically designed to decompose complex tasks into reusable skills. Yet, in practice, they still struggle unless trained on exhaustive layout variations.

A novel approach, detailed in a recent technical discussion, flips this paradigm on its head. Instead of training a single end-to-end policy or a hierarchical policy that adapts online, the method trains a small, curated set of discrete skills—such as "find the correct key," "go to the passage," "open the correct door," and "reach the goal." Each skill is trained once, then frozen. When a new layout is encountered, nothing updates. The system retrieves the appropriate pre-trained skills from long-term memory and composes them on the fly.

The elegance lies in its efficiency. The state space, while large when treated symbolically (roughly 360,000 distinct logical states from conservative counting—50 reachable cells for the agent, 50 for the key, 4 door configurations, multiple passage layouts, 3 inventory values, and 4 headings), is never fully explored during composition. The system only reuses states it actually encountered during skill training. There are no gradients, no online policy adaptation, and no fine-tuning. The skills are static building blocks, assembled to solve novel problems.

The results are striking. In a benchmark of 2,500 zero-shot episodes with randomized keys, passages, and door configurations—no retraining—the system achieved a 94% solve rate. Frozen skills. New layouts. Still works. This performance gap between memorization and true generalization is the core of the discussion. "If hierarchical RL should solve this, why does it still struggle with such a tiny, structured world unless you train it across every variation?" the author asks. "Or am I wrong?"

The implications are profound. Traditional RL agents, even sophisticated ones, often fail to generalize because they lack explicit abstractions. They treat each layout variation as a new problem, requiring extensive retraining. In contrast, the frozen skill approach leverages compositionality—a cornerstone of human intelligence—where learned components are reused flexibly. This suggests that generalization in RL may not require massive neural networks or endless exploration, but rather a structured decomposition of knowledge into reusable, non-negotiable skills.

The critical question remains: what is actually being learned when a system generalizes to layouts it has never seen? Is it symbolic reasoning? State abstraction? Or something else? The gap between "this looks trivial" and "most agents don't generalize" feels like the crux of the problem. As AI moves toward more complex environments, techniques that enable robust generalization without catastrophic forgetting or exhaustive retraining will be essential. The frozen skill approach isn't just a clever hack for grid worlds—it's a blueprint for building agents that can think, not just react.

"The gap between, this looks trivial and most agents don't generalise, feels like the interesting thing here." — Source: Hacker News Discussion

This method challenges the RL orthodoxy, suggesting that the path to generalization may lie not in bigger models or more data, but in smarter, more modular architectures. It's a reminder that in the quest for AI, sometimes the most breakthrough ideas come from rethinking the fundamentals.