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首页> 外文会议>4th Pacific Rim International Workshop on Muli-Agents, PRIMA 2001, 4th, Jul 28-29, 2001, Taipei, Taiwan >Market Performance of Adaptive Trading Agents in Synchronous Double Auctions
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Market Performance of Adaptive Trading Agents in Synchronous Double Auctions

机译：同步双拍卖中自适应交易代理的市场表现

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摘要

We are concerned with the issues on designing adaptive trading agents to leam bidding strategies in electronic market places. The synchronous double auction is used as a simulation testbed. We implemented agents with neural-network-based reinforcement learning called Q-leaming agents (QLA) to learn bidding strategies in the double auctions. In order to compare the performances of QLAs in the electronic market places, we also implemented many kinds of non-adaptive trading agents such as simple random bidding agents (SRBA), gradient-based greedy agent (GBGA), and truth telling agent (TTA). Instead of learning to model other trading agents that is computational intractable, we designed learning agents to model the market environment as a whole instead. Our experimental results showed that in terms of global market efficiency, QLAs could outperform TTAs and GBGAs but could not outperform SRBAs in the market of homogeneous type of agents. In terms of individual performance, QLAs could outperform all three non-adaptive trading agents when the opponents they are dealing with in the market place are a purely homogeneous type of non-adaptive trading agents. However, QLAs could only outperform TTAs and GBGAs and could not outperform SRBAs in the market of heterogeneous types of agents.
机译：我们关注在电子市场上设计适应性交易代理以学习竞价策略的问题。同步双拍卖被用作模拟测试平台。我们通过基于神经网络的强化学习（称为Q学习代理（QLA））实施了代理商，以在两次拍卖中学习出价策略。为了比较QLA在电子市场中的表现，我们还实现了多种非自适应交易代理，例如简单随机竞标代理（SRBA），基于梯度的贪婪代理（GBGA）和真相告诉代理（TTA） ）。我们没有学习建模难以计算的其他交易代理，而是设计了学习代理来模拟整个市场环境。我们的实验结果表明，就全球市场效率而言，在同类代理市场中，QLA的表现可能优于TTA和GBGA，但不能超过SRBA。就个人绩效而言，当在市场上与之交易的对手是完全同质的非自适应交易代理时，QLA的表现可能优于所有三个非自适应交易代理。但是，在异构类型的代理市场中，QLA仅能胜过TTA和GBGA，而不能胜过SRBA。

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