D4RL
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Benchmarks
D4RL
Most implemented
Decision Transformer: Reinforcement Learning via Sequence Modeling
Offline Reinforcement Learning with Implicit Q-Learning
Reformer: The Efficient Transformer
Rethinking Attention with Performers
D4RL: Datasets for Deep Data-Driven Reinforcement Learning
Papers
From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning
Offline Reinforcement Learning (RL) aims to learn effective policies from a static dataset without requiring further agent-environment interactions. However, its practical adoption is often hindered by the need for expli…
D4RLOffline RLreinforcement-learningReinforcement Learning+1Accelerating Residual Reinforcement Learning with Uncertainty Estimation
Residual Reinforcement Learning (RL) is a popular approach for adapting pretrained policies by learning a lightweight residual policy that provides corrective actions. While Residual RL is more sample-efficient than fine…
D4RLreinforcement-learningReinforcement LearningReinforcement Learning (RL)CAWR: Corruption-Averse Advantage-Weighted Regression for Robust Policy Optimization
Offline reinforcement learning (offline RL) algorithms often require additional constraints or penalty terms to address distribution shift issues, such as adding implicit or explicit policy constraints during policy opti…
D4RLOffline RLregressionMOORL: A Framework for Integrating Offline-Online Reinforcement Learning
Sample efficiency and exploration remain critical challenges in Deep Reinforcement Learning (DRL), particularly in complex domains. Offline RL, which enables agents to learn optimal policies from static, pre-collected da…
D4RLDeep Reinforcement LearningEfficient ExplorationOffline RL+2Offline RL with Smooth OOD Generalization in Convex Hull and its Neighborhood
Offline Reinforcement Learning (RL) struggles with distributional shifts, leading to the $Q$-value overestimation for out-of-distribution (OOD) actions. Existing methods address this issue by imposing constraints; howeve…
Computational EfficiencyD4RLOffline RLReinforcement Learning (RL)Policy-Based Trajectory Clustering in Offline Reinforcement Learning
We introduce a novel task of clustering trajectories from offline reinforcement learning (RL) datasets, where each cluster center represents the policy that generated its trajectories. By leveraging the connection betwee…
ClusteringD4RLOffline RLreinforcement-learning+4