  Digital Twin (DT) technologies, which aim to build digital replicas of
physical entities, are the key to providing efficient, concurrent simulation
and analysis of real-world objects. In displaying DTs, Holographic-Type
Communication (HTC), which supports the transmission of holographic data such
as Light Field (LF), can provide an immersive way for users to interact with
Holographic DTs (HDT). However, it is challenging to effectively allocate
interactive and resource-intensive HDT services among HDT users and providers.
In this paper, we integrate the paradigms of HTC and DT to form a HTC for DT
system, design a marketplace for HDT services where HDT users' and providers'
prices are evaluated by their valuation functions, and propose an auction-based
mechanism to match HDT services using a learning-based Double Dutch Auction
(DDA). Specifically, we apply DDA and train an agent acting as the auctioneer
to adjust the auction clock dynamically using Deep Reinforcement Learning
(DRL), aiming to achieve the best market efficiency. Simulation results
demonstrate that the proposed learning-based auctioneer can achieve
near-optimal social welfare at halved auction information exchange cost of the
baseline method.
