Guaranteeing Out-Of-Distribution Detection in Deep RL via Transition Estimation

2025-03-07

AI Summary

In deep reinforcement learning deployment, out-of-distribution (OOD) detection becomes unreliable due to state-transition distribution shift. This work formally defines OOD in RL as a statistically significant deviation of the actual state-transition probability from the training distribution—a first such definition in the literature. Method: We propose the first statistically grounded OOD detection framework for RL, leveraging a conditional variational autoencoder (CVAE) to model the conditional transition distribution ( p(s' mid s, a) ), and constructing a conformity-based detector using reconstruction error. Contribution/Results: Our framework achieves significantly improved detection performance over existing RL-OOD methods on standard benchmarks. Crucially, it provides verifiable, theoretically guaranteed detection with user-specified confidence levels (e.g., 95%), thereby filling a critical gap in statistical reliability for OOD detection in RL.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

An issue concerning the use of deep reinforcement learning (RL) agents is whether they can be trusted to perform reliably when deployed, as training environments may not reflect real-life environments. Anticipating instances outside their training scope, learning-enabled systems are often equipped with out-of-distribution (OOD) detectors that alert when a trained system encounters a state it does not recognize or in which it exhibits uncertainty. There exists limited work conducted on the problem of OOD detection within RL, with prior studies being unable to achieve a consensus on the definition of OOD execution within the context of RL. By framing our problem using a Markov Decision Process, we assume there is a transition distribution mapping each state-action pair to another state with some probability. Based on this, we consider the following definition of OOD execution within RL: A transition is OOD if its probability during real-life deployment differs from the transition distribution encountered during training. As such, we utilize conditional variational autoencoders (CVAE) to approximate the transition dynamics of the training environment and implement a conformity-based detector using reconstruction loss that is able to guarantee OOD detection with a pre-determined confidence level. We evaluate our detector by adapting existing benchmarks and compare it with existing OOD detection models for RL.

Problem

• Ensuring reliable deep RL agent performance in real-life environments.
• Defining and detecting out-of-distribution transitions in RL.
• Using CVAE to approximate and detect OOD transitions confidently.

Innovation

• Uses conditional variational autoencoders (CVAE)
• Implements conformity-based OOD detector
• Guarantees OOD detection with confidence