Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer

Methods of deep machine learning enable to to reuse low-level representations efficiently for generating more abstract high-level representations. Originally, deep learning has been applied passively (e.g., for classification purposes). Recently, it has been extended to estimate the value of actions for autonomous agents within the framework of reinforcement learning (RL). Explicit models of the environment can be learned to augment such a value function. Although “flat” connectionist methods have already been used for model-based RL, up to now, only model-free variants of RL have been equipped with methods from deep learning. We propose a variant of deep model-based RL that enables an agent to learn arbitrarily abstract hierarchical representations of its environment. In this paper, we present research on how such hierarchical representations can be grounded in sensorimotor interaction between an agent and its environment.

💡 Research Summary

The paper tackles a fundamental limitation of contemporary deep reinforcement learning (RL) systems: the reliance on pre‑defined, often hand‑crafted state representations that impose a heavy semantic load and hinder autonomous knowledge transfer across tasks. The authors argue that true autonomy requires agents to construct their own abstract representations from raw sensor‑motor interaction, rather than being supplied with a fixed set of world states. To this end, they propose a novel hierarchical model‑based RL architecture that integrates deep representation learning with a planning‑oriented transition model.

The theoretical foundation begins with a review of Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs). While traditional approaches treat the world state as given (objective interaction), the authors distinguish a “subjective interaction” regime where the agent only perceives limited, local information (e.g., the occupancy of neighboring cells) and must infer higher‑level states internally. They contend that objective interaction leads to semantic overload because the designer must anticipate which world states are relevant, whereas subjective interaction naturally encourages the emergence of compact, reusable representations.

The core technical contribution consists of two tightly coupled components. First, a deep representation learner (implemented with stacked auto‑encoders, RBMs, or similar deep networks) compresses low‑level sensor data into a hierarchy of increasingly abstract latent variables. Each layer reduces data dimensionality logarithmically while preserving essential structure, thereby forming a ladder of representations from concrete sensory patches to high‑level concepts. Second, on top of this latent space the authors train a model‑based RL module: a transition function T : Z × A → Z that predicts the next latent state given the current latent state Z and action A, and a value function V : Z × A → ℝ that estimates expected cumulative reward. Because T is learned, the agent can simulate future trajectories arbitrarily far into the imagined latent space, enabling planning without exhaustive real‑world exploration. The policy π = (V, T) thus operates entirely on the agent‑generated abstract states.

To demonstrate the practical impact of subjective versus objective interaction, the authors construct a grid‑world “corridor” environment. The training world is a small 5 × 5 maze; the test world adds a new corridor segment that introduces previously unseen absolute coordinates. Two input modalities are compared: (1) objective input (the agent’s absolute (x, y) position) and (2) subjective input (the occupancy pattern of the four adjacent cells). In the objective case, the new segment forces the agent to relearn the transition model because the absolute state space has changed. In contrast, the subjective representation remains identical across the old and new segments; consequently, the transition and value functions learned in training transfer seamlessly, and the agent quickly adapts to the larger environment without additional learning. Empirical results show a marked reduction in episodes required to achieve optimal performance when using subjective inputs, confirming that the hierarchical architecture supports robust knowledge transfer.

Beyond the experimental validation, the paper situates its contribution within broader discussions of “semantic load” and “knowledge transfer.” The authors argue that these are two sides of the same issue: semantic load arises when designers inject domain knowledge via fixed representations, which simultaneously blocks autonomous transfer. By letting the agent discover its own representations, the system reduces semantic load and enables knowledge transfer as a natural by‑product.

The paper’s contributions can be summarized as follows:

1. A principled argument for abandoning pre‑specified world states in favor of agent‑generated latent representations.
2. A concrete hierarchical architecture that couples deep unsupervised representation learning with model‑based RL, allowing planning in abstract latent space.
3. Empirical evidence that subjective, locally‑derived observations facilitate zero‑shot transfer to novel environments, whereas objective observations do not.
4. A conceptual framework linking semantic load, representation grounding, and knowledge transfer in reinforcement learning.

Future work outlined includes extending the approach to continuous high‑dimensional sensory streams (e.g., vision, lidar), scaling to multi‑agent scenarios where shared hierarchical representations could emerge, deploying the system on physical robots to test robustness against noise and dynamics, and developing joint optimization schemes that learn T and V simultaneously in an end‑to‑end fashion. Such extensions would test the generality of the proposed method and move the field closer to truly autonomous, transferable reinforcement learning agents.