D4RL supplies new offline RL benchmarks and datasets from expert and mixed sources to expose weaknesses in existing algorithms and standardize evaluation.
Deep imitative models for flexible inference, planning, and control
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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NeuroTrajectory is a neuroevolutionary method that trains deep neural networks via genetic algorithms to estimate multi-objective optimal state trajectories over a finite horizon for autonomous vehicle motion planning.
Offline RL promises to extract high-utility policies from static datasets but faces fundamental challenges that current methods only partially address.
citing papers explorer
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D4RL: Datasets for Deep Data-Driven Reinforcement Learning
D4RL supplies new offline RL benchmarks and datasets from expert and mixed sources to expose weaknesses in existing algorithms and standardize evaluation.
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NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehicles
NeuroTrajectory is a neuroevolutionary method that trains deep neural networks via genetic algorithms to estimate multi-objective optimal state trajectories over a finite horizon for autonomous vehicle motion planning.
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Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Offline RL promises to extract high-utility policies from static datasets but faces fundamental challenges that current methods only partially address.