Introduction to RL-CP Fusion and its core concepts
RL-CP Fusion combines Offline Reinforcement Learning with Conformal Prediction to create robust and reliable learning algorithms with uncertainty quantification.
Reinforcement Learning (RL) has emerged as a versatile paradigm in the field of machine learning, capable of tackling complex decision-making problems where agents learn to optimize long-term rewards through interactions with an environment. From language model alignment to video game playing, RL has demonstrated immense success, especially in environments that enjoy no penalty for exploratory action.
Unfortunately, real-world applications often present constraints where direct interaction with the environment is either impractical, prohibitively expensive, or downright dangerous. In the absence of simulators, Offline or Batch RL attempts to sidestep this by garnering large datasets upstream of train time and applying slightly adjusted versions of the Online techniques -- albeit to varying degrees of success. The catch: no dynamic data collection or exploration during training. The offline setting requires learning effective policies solely from a static dataset -- presenting a vast set of new challenges.
A common strategy in data-constrained settings is bootstrapping. However, employing standard value-based off-policy RL algorithms with bootstrapping frequently leads to poor performance. This approach tends to produce overly optimistic value function estimates, particularly for sparse or out-of-distribution (OOD) actions, known as extrapolation error. To address this, previous work has incorporated complex regularization terms into model objectives, aiming to establish lower bounds on both Q-function estimates and policy performance.
Our project takes a different approach by integrating Conformal Prediction (CP)—a statistical tool for quantifying uncertainty—to enhance the stability and reliability of Offline RL. By leveraging CP, we aim to generate robust confidence intervals for Q-value estimates, particularly for optimistic predictions. This helps mitigate the risk of overestimating OOD actions and ensures a more stable learning process, while also providing meaningful coverage guarantees.
Learn effective policies from static datasets without direct environment interaction.
Leverage Conformal Prediction for robust confidence intervals on Q-value estimates.
Mitigate overestimation risks and ensure a more stable learning process.
To get started with RL-CP Fusion, follow these steps: