Offline RL
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Benchmarks
Most implemented
Decision Transformer: Reinforcement Learning via Sequence Modeling
Conservative Q-Learning for Offline Reinforcement Learning
Offline Reinforcement Learning with Implicit Q-Learning
Reformer: The Efficient Transformer
A Minimalist Approach to Offline Reinforcement Learning
Papers
Towards Optimal Policy Improvement
Practical Reinforcement Learning (RL) algorithms learn to solve Markov Decision Processes (MDPs) through iterative policy improvement in the presence of approximate evaluation. We study policy improvement from first prin…
Reinforcement LearningOffline RLDecision Titan: Test-Time Training for Long-Term Memory in Offline Reinforcement Learning
Long-term dependencies remain a major challenge for sequential decision-making in the field of AI: RNNs suffer from vanishing gradients and the limited expressivity of vector-based hidden states, whilst Transformer-based…
Reinforcement LearningOffline RLX-Tree: Tokenizing Reusable Experience for Efficient Agent Generalization
Multi-step agents are trained on flat action streams: SFT and RLVR weight every token uniformly and ignore the sub-procedures that recur across tasks, the hierarchy that lets humans plan top-down from reusable routines. …
Offline RLConfounding Masquerading as Improvement: A Systematic Evaluation of Offline Reinforcement Learning for Stroke Antithrombotic Treatment in a 129,000-Patient Registry
Recent offline reinforcement learning (RL) studies report policies that outperform physician decisions on clinical outcomes. We conduct a systematic, partially crossed evaluation of five offline RL algorithm families and…
Reinforcement LearningOffline RLLearning to Allocate Incentives for Incentivized Advertising via Offline Model-Based Reinforcement Learning
Complete your ad view and grab a 5-cent bonus! In incentivized advertising, a platform promises users a bonus before observing downstream ad revenue, encouraging them to click and complete ads. It must balance the incent…
Reinforcement LearningCausal InferenceOffline RLSimple Actors and Deep Critics for Scalable Reinforcement Learning
Recent progress in offline reinforcement learning (RL) has been driven by expressive generative actors such as diffusion and flow-matching policies, which capture multimodal behavior in offline datasets. However, these a…
Reinforcement LearningOffline RL