Algorithmic Collusion in Dynamic Pricing with Deep Reinforcement Learning
By Shidi Deng, Maximilian Schiffer, Martin Bichler
Rating
1423
Battle Count: 85
Relevance
7/10
Provides insights into algorithmic pricing strategies and potential for tacit collusion, relevant for market making and algorithmic trading
Implementation Complexity
6/10
Requires implementation of multiple RL algorithms and market models, but builds on established frameworks
Reproducibility
4/5
Detailed experimental setup and hyperparameters provided, code implementation available
About this paper
Methodology: Deep Reinforcement Learning. Problem types: Reinforcement Learning, Dynamic Pricing, Market Competition.
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