Causal Reinforcement Learning using Observational and Interventional Data (2106.14421v1)

Abstract: Learning efficiently a causal model of the environment is a key challenge of model-based RL agents operating in POMDPs. We consider here a scenario where the learning agent has the ability to collect online experiences through direct interactions with the environment (interventional data), but has also access to a large collection of offline experiences, obtained by observing another agent interacting with the environment (observational data). A key ingredient, that makes this situation non-trivial, is that we allow the observed agent to interact with the environment based on hidden information, which is not observed by the learning agent. We then ask the following questions: can the online and offline experiences be safely combined for learning a causal model ? And can we expect the offline experiences to improve the agent's performances ? To answer these questions, we import ideas from the well-established causal framework of do-calculus, and we express model-based reinforcement learning as a causal inference problem. Then, we propose a general yet simple methodology for leveraging offline data during learning. In a nutshell, the method relies on learning a latent-based causal transition model that explains both the interventional and observational regimes, and then using the recovered latent variable to infer the standard POMDP transition model via deconfounding. We prove our method is correct and efficient in the sense that it attains better generalization guarantees due to the offline data (in the asymptotic case), and we illustrate its effectiveness empirically on synthetic toy problems. Our contribution aims at bridging the gap between the fields of reinforcement learning and causality.

• The paper formalizes model-based reinforcement learning as a causal inference problem using do-calculus to integrate both observational and interventional data.
• It introduces a latent-based causal transition model that deconfounds hidden variables, ensuring unbiased estimation and superior generalization.
• Empirical studies on synthetic problems validate the methodology’s potential for robust performance in partially-observable environments.

Insights into Causal Reinforcement Learning using Observational and Interventional Data

The paper "Causal Reinforcement Learning using Observational and Interventional Data" addresses a complex challenge in the domain of model-based reinforcement learning (RL) agents operating within Partially-Observable Markov Decision Processes (POMDPs). The focus is on the efficient learning of causal models of the environment, particularly when agents have access to both online, directly-interacted (interventional) experiences, and offline, observed (observational) experiences. This dual-scenario introduces significant complexity as the observed agent may base its actions on hidden information unavailable to the learning agent. The core inquiry revolves around the feasibility and safety of combining these experiential datasets to enhance a causal model and whether observational data can boost the agent's performance.

By leveraging the framework of do-calculus, the authors translate the RL problem into one of causal inference, thereby bridging the conceptual gap between reinforcement learning and causality. The methodology presented involves learning a latent-based causal transition model that accounts for both interventional and observational data regimes. This model utilizes recovered latent variables to infer the POMDP transition model, addressing potential confounding issues by deconfounding techniques.

Main Contributions

1. Causal Formulation of Model-Based RL: The paper formally casts model-based RL as a causal inference challenge by employing do-calculus, thus providing a structured approach to address the integration of offline and online scenarios within RL.
2. Proposed Methodology: A generic methodology is proposed for the amalgamation of offline and online data within model-based RL frameworks. This method is theoretically proven to be correct, ensuring unbiased estimations, and efficient, demonstrating superior generalization guarantees asymptotically.
3. Empirical Illustrations: Practical implementations of the methodology validate its efficacy using synthetic problems, showcasing the method's potential in achieving better transition models compared to approaches that solely rely on online data or naive combinations of data without accounting for confounders.

Theoretical and Practical Implications

The presented work has significant theoretical implications as it extends the boundaries of RL by utilizing causal inference methods, specifically do-calculus, which allows for a more nuanced understanding of the underlying environmental models. Practically, the methodology offers a robust framework for leveraging vast amounts of observational data that could otherwise be underutilized or lead to erroneous conclusions if misinterpreted as purely interventional data. This has significant applications in areas such as autonomous driving and medical treatment systems, where inherent observational biases must be correctly accounted for to derive accurate causal models.

By conceptualizing reinforcement learning through the lens of causality, this work adds a substantial layer of sophistication and accuracy to how agents perceive and interact with their environments. Future extensions could explore how this methodology can be applied to model-free RL or expand upon its utility for guiding and enhancing online exploration strategies in RL settings.

Overall, the paper's contributions lay the groundwork for more reliable and effective RL systems through the integration of observational data, overcoming common challenges associated with partial observability and confounding in complex, real-world systems.