A Novel Bidding Strategy for PDAs Using MCTS in Continuous Action Spaces

Bidding in a periodic double auction (PDA) is challenging due to its sequential nature, where one needs to consider current as well as future auctions to decide the bids. Monte-Carlo Tree Search (MCTS), which is a state-of-the-art online planning algorithm for tackling sequential problems, seems a perfect fit for bidding in PDAs. However, the success stories of MCTS are largely limited to discrete action spaces, and its efficacy diminishes when dealing with continuous actions. Conventional methods often resort to overly simplistic discretizations that limit exploration and fail to provide valuable insights into unexplored actions. In this work, we propose a novel bidding strategy for PDAs, Regression-MCTS, that is built upon MCTS for a continuous action space of bid prices. Unlike conventional methods, our novel MCTS method leverages information obtained from explored actions to enhance the understanding of the larger action set within the continuous domain to place bids in the auctions, thus generalizing the information about action quality between a wider action space for faster learning. To test the efficacy of our proposed method, we design an efficient PDA simulator that closely resembles real-world PDAs. Our analysis verifies that the increase in the number of rollouts improves its performance. Furthermore, our experimental results demonstrate that our approach outperforms existing MCTS-based bidding strategies and the majority of state-of-the-art PDA bidding strategies, showcasing its superior performance in PDAs.