offline-reinforcement-learning

This paper proposes a novel offline reinforcement learning framework for active flow control that uses a sensor position-conditioned architecture with Point Attention layers to handle varying sensor configurations, enabling data-driven policy extraction without costly online interactions.

This paper presents an offline reinforcement learning framework for optimizing SLAM throughput control in warehouse fulfillment environments, balancing throughput maximization with downstream stability. The approach is algorithm-agnostic and demonstrates that the CQL policy improves system health by 22.97% and reduces throttling duration by 3.18%.

This paper introduces DRIVE, a unified Transformer-based framework for offline auto-bidding that decouples candidate action generation from decision making, combining distributional action modeling, retrieval-augmented candidate generation, and value-based evaluation to improve bidding performance under budget and cost constraints.

This paper introduces RL4F, an offline reinforcement learning benchmark for plasma control in nuclear fusion, providing closed-loop evaluation environments and baseline comparisons across four profile tracking tasks using real tokamak data from DIII-D. The codebase and datasets are open-sourced to foster further research.

This paper proposes a three-stage diagnostic framework to identify why offline model selectors fail to beat the best single model, applying it to dropout prediction on edX clickstream data. The study finds that the bottleneck is local representational ambiguity rather than learner choice or distribution shift, recommending state redesign or new data collection over further algorithm tuning.

This paper introduces Posterior Hybrid Bayesian Belief (PhyB), a framework that reformulates the expectation in Bayesian RL as a convex combination over dynamics models, enabling efficient regularized offline policy optimization with bounded objective discrepancy and state-of-the-art performance.

Proposes DriftQL, which combines a drift-based behavioral regularizer with critic-driven policy improvement for offline RL, outperforming diffusion and flow methods on D4RL and OGBench while maintaining simplicity and efficiency.

This paper introduces DOSER, a framework using diffusion models for out-of-distribution detection and selective regularization in offline reinforcement learning. It aims to improve performance on static datasets by distinguishing between beneficial and detrimental OOD actions.