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Improving Zero-Shot Offline RL via Behavioral Task... | AI Research

Key Takeaways

  • Improving Zero-Shot Offline RL via Behavioral Task Sampling Offline zero-shot reinforcement learning (RL) aims to train agents that can perform new, unseen t...
  • Offline zero-shot reinforcement learning (RL) aims to learn agents that optimize unseen reward functions without additional environment interaction.
  • The standard approach to this problem trains task-conditioned policies by sampling task vectors that define linear reward functions over learned state representations.
  • In most existing algorithms, these task vectors are randomly sampled, implicitly assuming this adequately captures the structure of the task space.
  • We argue that doing so leads to suboptimal zero-shot generalization.
Paper AbstractExpand

Offline zero-shot reinforcement learning (RL) aims to learn agents that optimize unseen reward functions without additional environment interaction. The standard approach to this problem trains task-conditioned policies by sampling task vectors that define linear reward functions over learned state representations. In most existing algorithms, these task vectors are randomly sampled, implicitly assuming this adequately captures the structure of the task space. We argue that doing so leads to suboptimal zero-shot generalization. To address this limitation, we propose extracting task vectors directly from the offline dataset and using them to define the task distribution used for policy training. We introduce a simple and general reward function extraction procedure that integrates into existing offline zero-shot RL algorithms. Across multiple benchmark environments and baselines, our approach improves zero-shot performance by an average of 20%, highlighting the importance of principled task sampling in offline zero-shot RL.

Improving Zero-Shot Offline RL via Behavioral Task Sampling
Offline zero-shot reinforcement learning (RL) aims to train agents that can perform new, unseen tasks without needing to interact with the environment again. Typically, these agents learn by practicing on a wide range of "task vectors"—mathematical instructions that define different reward goals. Most current methods generate these tasks by picking directions at random. This paper argues that random selection is inefficient because it often creates tasks that are physically impossible or irrelevant to the environment, leading to poor performance. Instead, the authors propose a new method that extracts task vectors directly from existing offline data to ensure the agent only trains on tasks that are actually achievable.

The Problem with Random Tasks

In standard zero-shot RL, agents are trained using task vectors sampled uniformly from a high-dimensional space. The authors demonstrate that this approach suffers from "signal dilution." As the complexity of the environment increases, these randomly chosen tasks tend to become mathematically orthogonal to the behaviors the agent is actually capable of performing. Because the reward signal becomes so weak and noisy, the agent struggles to distinguish between effective and ineffective behaviors, which ultimately hinders its ability to generalize to new tasks.

Extracting Tasks from Data

To solve this, the researchers introduced a procedure called Behavioral Task Sampling. Instead of relying on random chance, they analyze the offline dataset to identify the "feature occupancy"—a measure of which state features are actually visited during real-world trajectories. By calculating task vectors based on these observed behaviors, the training process focuses on tasks that are grounded in the physics and dynamics of the environment. This ensures that the agent spends its training time learning to optimize for goals that are both meaningful and attainable.

Significant Performance Gains

This approach is designed to be method-agnostic, meaning it can be integrated into existing offline RL frameworks without requiring major changes to how they learn state representations. When tested across multiple benchmark environments, the researchers found that replacing random task sampling with their data-driven behavioral distribution resulted in an average performance improvement of 20%.

Key Takeaways

The core insight of this research is that the quality of an agent's training is heavily dependent on the distribution of the tasks it practices. By moving away from uniform, random sampling and toward a principled, data-driven approach, the agent gains a more stable and informative learning signal. This research highlights that for zero-shot RL to be effective, the "task space" must be closely aligned with the "behavioral space" of the environment.

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