arXiv AI
AI-driven Prices for Externalities and Sustainability in Production Markets
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How language model agents reshape price competition
arXiv:2610.21711v1 Announce Type: cross Abstract: Repeated Bertrand competition is a stringent benchmark for algorithmic pricing: in homogeneous-goods markets, standard learning procedures typically...
arXiv AI
Jul 1
Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches
arXiv:2606. 31347v1 Announce Type: new Abstract: The electrification of transportation through electric vehicles introduces new challenges for power grid management, such as increased peak demand, voltage fluctuations, line overloads, and the integration of variable renewable energy sources.
arXiv AI
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Financial Context-Rich Strategy for Actor-Critic based Agents: A Deep Reinforcement Learning Approach for Stock Trading
The paper proposes a deep reinforcement learning framework for stock trading that incorporates sentiment analysis into actor‑critic agents. It compares pure policy‑optimization methods (A2C, PPO) with hybrid approaches that combine policy optimization and Q‑learning (DDPG, TD3, SAC). Results show that the hybrid agents consistently outperform others in profit maximization and risk‑averse trading when sentiment is deeply infused.
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From Confounding to Learning: Dynamic Service Fee Pricing on Third-Party Platforms
arXiv:2512. 22749v2 Announce Type: replace Abstract: We study the pricing behavior of third-party platforms facing strategic agents.
arXiv AI
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AI agents in Algorithmic Electricity Markets: On the Emergence of Tacit Collusion
The paper examines whether autonomous learning-based agents in electricity markets can develop tacit collusion without explicit coordination. By modeling strategic bidding as a repeated game with imperfect public monitoring and employing multi-agent reinforcement learning, the authors identify conditions under which agents achieve supra-competitive outcomes. Their experiments demonstrate that such collusive behavior can emerge naturally, highlighting a realistic risk for algorithmic electricity markets.
arXiv Machine Learning
Oct 23
Fast Iterative Combinatorial Auctions via Deep Reinforcement Learning
FastCombo is a deep reinforcement learning method for iterative combinatorial auctions that learns price policies offline and applies them online with a single neural‑network forward pass. By formulating the problem as a partially observable Markov decision process, it assigns credit to queries that enable future decisions, thereby reducing the expected number of auction rounds. Experiments show that FastCombo achieves competitive clearing while cutting online price‑selection time by roughly 300–900 times compared to a standard Bayesian baseline.