Abstract
Deployment efficiency is an important criterion for many real-world applications of reinforcement learning (RL). Despite the community's increasing interest, there lacks a formal theoretical formulation for the problem. In this paper, we propose such a formulation for deployment-efficient RL (DE-RL) from an “optimization with constraints” perspective: we are interested in exploring an MDP and obtaining a near-optimal policy within minimal deployment complexity, whereas in each deployment the policy can sample a large batch of data. Using finite-horizon linear MDPs as a concrete structural model, we reveal the fundamental limit in achieving deployment efficiency by establishing information-theoretic lower bounds, and provide algorithms that achieve the optimal deployment efficiency. Moreover, our formulation for DE-RL is flexible and can serve as a building block for other practically relevant settings; we give “Safe DE-RL” and “Sample-Efficient DE-RL” as two examples, which may be worth future investigation.
Original language | English (US) |
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State | Published - 2022 |
Event | 10th International Conference on Learning Representations, ICLR 2022 - Virtual, Online Duration: Apr 25 2022 → Apr 29 2022 |
Conference
Conference | 10th International Conference on Learning Representations, ICLR 2022 |
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City | Virtual, Online |
Period | 4/25/22 → 4/29/22 |
ASJC Scopus subject areas
- Language and Linguistics
- Computer Science Applications
- Education
- Linguistics and Language