Anikait Singh

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning

2023 · arXiv (Cornell University)

A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization from existing datasets followed by fast online fine-tuning with limited interaction. However, existing offline RL methods tend to behave poorly …

D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning

2024 · arXiv (Cornell University)

Offline reinforcement learning algorithms hold the promise of enabling data-driven RL methods that do not require costly or dangerous real-world exploration and benefit from large pre-collected datasets. This in turn can facilitate real-world applications, as …