Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets
This work addresses wireless network optimization for improved resource management, but it is incremental as it builds on existing offline RL methods.
The paper tackles the problem of wireless radio resource management by applying offline reinforcement learning to avoid performance loss from online exploration, and demonstrates that a novel method using mixed datasets from suboptimal policies can achieve near-optimal performance.
The recent development of reinforcement learning (RL) has boosted the adoption of online RL for wireless radio resource management (RRM). However, online RL algorithms require direct interactions with the environment, which may be undesirable given the potential performance loss due to the unavoidable exploration in RL. In this work, we first investigate the use of \emph{offline} RL algorithms in solving the RRM problem. We evaluate several state-of-the-art offline RL algorithms, including behavior constrained Q-learning (BCQ), conservative Q-learning (CQL), and implicit Q-learning (IQL), for a specific RRM problem that aims at maximizing a linear combination {of sum and} 5-percentile rates via user scheduling. We observe that the performance of offline RL for the RRM problem depends critically on the behavior policy used for data collection, and further propose a novel offline RL solution that leverages heterogeneous datasets collected by different behavior policies. We show that with a proper mixture of the datasets, offline RL can produce a near-optimal RL policy even when all involved behavior policies are highly suboptimal.