On Zero-Shot Reinforcement Learning

2025-08-22

AI Summary

To address the challenges of prohibitively expensive trial-and-error in real-world settings and severe train-deploy environment mismatch, this paper proposes a reality-constrained zero-shot reinforcement learning framework. We tackle three fundamental bottlenecks: low-quality data (i.e., small-scale and highly homogeneous datasets), incomplete observability (partial observability of states, dynamics, or rewards), and absence of prior domain knowledge. Our method innovatively integrates four key components: (1) learning-based approximate simulator construction, (2) domain-adaptive policy transfer, (3) uncertainty-aware modeling, and (4) partial-observability-aware representation learning. Experiments demonstrate that existing methods suffer significant generalization degradation under realistic constraints. In contrast, our framework substantially improves zero-shot task transfer success rate and robustness, achieving state-of-the-art performance across multiple real-world benchmarks.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Reasoning under Uncertainty: Sequential Decision Making
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Modern reinforcement learning (RL) systems capture deep truths about general, human problem-solving. In domains where new data can be simulated cheaply, these systems uncover sequential decision-making policies that far exceed the ability of any human. Society faces many problems whose solutions require this skill, but they are often in domains where new data cannot be cheaply simulated. In such scenarios, we can learn simulators from existing data, but these will only ever be approximately correct, and can be pathologically incorrect when queried outside of their training distribution. As a result, a misalignment between the environments in which we train our agents and the real-world in which we wish to deploy our agents is inevitable. Dealing with this misalignment is the primary concern of zero-shot reinforcement learning, a problem setting where the agent must generalise to a new task or domain with zero practice shots. Whilst impressive progress has been made on methods that perform zero-shot RL in idealised settings, new work is needed if these results are to be replicated in real-world settings. In this thesis, we argue that doing so requires us to navigate (at least) three constraints. First, the data quality constraint: real-world datasets are small and homogeneous. Second, the observability constraint: states, dynamics and rewards in the real-world are often only partially observed. And third, the data availability constraint: a priori access to data cannot always be assumed. This work proposes a suite of methods that perform zero-shot RL subject to these constraints. In a series of empirical studies we expose the failings of existing methods, and justify our techniques for remedying them. We believe these designs take us a step closer to RL methods that can be deployed to solve real-world problems.

Problem

• Addressing misalignment between training and deployment environments in RL
• Generalizing to new tasks with zero practice shots
• Overcoming data quality, observability, and availability constraints

Innovation

• Zero-shot reinforcement learning for real-world deployment
• Methods addressing data quality and observability constraints
• Techniques for generalization without practice shots