Offline Reinforcement Learning

Offline reinforcement learning enables agents to learn optimal policies from fixed, pre-collected datasets without further interaction with the environment.

Offline Reinforcement Learning (Offline RL), also known as batch reinforcement learning, is a paradigm where an agent learns an optimal policy from a static dataset of previously collected transitions, such as $(s, a, r, s’)$. Unlike traditional online reinforcement learning, where the agent continuously interacts with the environment to explore and collect new data, Offline RL relies entirely on historical data, making it particularly valuable in scenarios where real-time exploration is costly, dangerous, or impractical.

The Offline Reinforcement Learning framework: learning optimal policies from fixed historical datasets without environment interaction.

Our research in this area addresses the fundamental challenges of Offline RL, particularly the issue of distribution shift. When a learned policy deviates significantly from the behavior policy that generated the dataset, the agent may encounter “out-of-distribution” (OOD) states and actions, leading to highly over-optimistic value estimations—a phenomenon known as extrapolation error. We investigate various state-of-the-art techniques to mitigate these errors, including:

  • Policy Constraint Methods: Restricting the learned policy to stay close to the behavior policy to avoid OOD actions.
  • Uncertainty-based Regularization: Incorporating uncertainty quantification to penalize value estimates for rarely seen state-action pairs.
  • Model-based Offline RL: Learning a world model from the dataset and performing safe planning or policy optimization within the learned model.

A key application of our Offline RL research is in Next-Generation Communication Networks. In real-world wireless systems, such as 5G/6G base stations or satellite networks, performing “trial-and-error” exploration (online RL) can disrupt services and degrade user experience. By utilizing vast amounts of historical telemetry and log data, we develop Offline RL algorithms for:

  • Intelligent Resource Allocation: Optimizing power, bandwidth, and beamforming parameters based on historical traffic patterns and channel conditions.
  • Autonomous Network Management: Learning robust handover and interference management policies from historical network operation data.
  • Energy-Efficient Scheduling: Developing scheduling policies that minimize power consumption while meeting strict Quality of Service (QoS) requirements, all without the risk of online exploration.

By bridging the gap between historical data and optimal decision-making, Offline RL serves as a critical enabler for deploying AI-driven solutions in safety-critical and high-reliability communication systems.