Improving Zero-Shot Offline RL via Behavioral Task Sampling

Improving Zero-Shot Generalization in Offline RL via Behavioral Task Sampling

Motivation and Problem Statement

Offline zero-shot reinforcement learning (ZSRL) seeks to train policies from fixed datasets such that, at deployment, an agent can optimize for novel, unseen reward functions without further environment interaction. The dominant approach builds on reward-conditioned policies or universal value function approximators (UVFA), operating over a parametric family of rewards—typically linear functions over learned state features. Existing pipelines in ZSRL (e.g., based on Successor Features, SF, or Forward-Backward, FB, models) uniformly sample task vectors from the unit hypersphere, implicitly assuming adequate coverage of relevant tasks.

This paper identifies a critical shortcoming in this sampling scheme: the geometric mismatch between the uniformly sampled task space and the constraint manifold defined by actual environment dynamics. The authors provide both theoretical and empirical analysis showing that, as the latent dimension grows, the reward signal for uniformly sampled tasks dilutes, ultimately vanishing ("signal dilution") due to concentration of measure. This impedes policy improvement and leads to weak out-of-distribution generalization.

Behavioral Task Distribution: Methodology

To address this limitation, the authors introduce Behavioral Task Distribution (BTD) sampling. The key insight is to extract task vectors directly from the offline dataset in a manner that reflects empirically achievable behaviors—task vectors are constructed by computing discounted feature occupancies of subtrajectories sampled from the dataset. A parametric density model (GMM) is then fitted to these empirical tasks, yielding BTD.

During policy learning, instead of sampling reward vectors uniformly, the algorithm samples from BTD. This approach is method-agnostic: it can be directly inserted into any offline ZSRL framework (SF or FB) without requiring changes to core representation learning objectives or architectures.

Theoretical Analysis

The authors formalize the effect of uniform task sampling in high-dimensional latent spaces. Uniform sampling makes the vast majority of tasks nearly orthogonal to the behavioral subspace induced by environment constraints, resulting in near-constant returns across all policies (i.e., negligible inter-policy variance of returns). They derive that, as latent space dimensionality ($d$) increases, the expected variance of returns approaches zero, making the agent's performance signal indistinguishable from noise.

In contrast, tasks derived from BTD are tightly aligned with the support of behaviors actually observed or achievable in the environment. This yields reward functions and gradients exhibiting meaningful variance among candidate policies, preserving a strong learning signal in high dimensions.

Experimental Results

Comprehensive experiments are conducted across standard ZSRL benchmarks (Cheetah, Walker, and Quadruped from the DeepMind Control Suite) using datasets from ExoRL. The authors consider a range of state encoders and policy learning pipelines (Autoencoders, Transition models, low-rank representation, BYOL/BYOL-y, FB) and evaluate across multiple latent feature dimensions.

Key empirical findings:

• Consistent and Significant Improvement: Across all methods and environments, BTD-sampling yields an average 20% improvement in zero-shot test performance over baselines using uniform sampling.
• Dimensional Robustness: As latent task dimensionality increases (up to $d=1000$), baseline methods suffer catastrophic failure due to signal dilution, while BTD-sampling maintains robust performance.
• Reduced Variance and Failure Rates: The gain is not merely in mean performance; BTD-sampling tightens the distribution, elevating the performance floor and removing most low-performing outlier runs.
• Additive Task Mixtures: Mixing uniform and BTD-sampled task vectors during policy training always results in performance degradation, empirically verifying that task vectors outside the behavioral manifold harm generalization.
• Ablations: Performance gains of BTD are confirmed not to stem from density modeling or other heuristics, but from properly aligning task sampling with data-driven behavioral support. The method shows only minor sensitivity to GMM parameterization, and visualizations confirm strong concentration of empirical task vectors in a small subset of the feature space.

Implications and Future Directions

The results have direct practical implications: effective ZSRL in high-dimensional feature spaces requires that task sampling be grounded in the actual achievable behaviors of the dataset/environment, not arbitrary directions in feature space. This reshapes the way generalist RL agents should be trained—task distribution design is as crucial as representation or algorithmic fidelity.

Theoretically, the formalization of "signal dilution" presents a principled explanation for generalization failures observed in high-dimensional offline RL. The work motivates further research on principled task space modeling, both in the offline and online settings. Extensions to domains with richer reward parameterizations, structured/decomposable reward families, or non-linear reward maps are natural avenues for exploration. Additionally, integrating BTD with curriculum learning or discovery of challenging test-aligned tasks could yield further generalization improvements.

Conclusion

This work establishes that uniform task sampling in offline ZSRL can fundamentally limit an agent's ability to generalize, especially in high-dimensional latent spaces. By constructing a data-driven Behavioral Task Distribution reflecting empirical behavioral support, policy learning becomes well-aligned with environment constraints, yielding substantial and robust gains in zero-shot policy performance. This work shifts the focus from solely improving representation learning or policy optimization to principled task distribution design, offering an actionable path toward more scalable and generalizable RL agents.

Reference:

"Improving Zero-Shot Offline RL via Behavioral Task Sampling" (2604.25496)