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: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.
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.
arXiv:2512. 22749v2 Announce Type: replace Abstract: We study the pricing behavior of third-party platforms facing strategic agents.
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.
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.
The paper tackles the data market design problem, which seeks signaling schemes that maximize revenue for an information seller. It applies deep learning to learn these schemes, addressing both obedience and incentive constraints, and demonstrates that the framework can replicate known theoretical solutions, extend to more complex scenarios, and suggest new optimal designs. The study builds on prior auction‑design work and introduces a novel approach for revenue‑optimal data markets.
arXiv:2610.07491v1 Announce Type: cross Abstract: When multiple agents share a cost budget, a common Lagrange multiplier can enforce the aggregate constraint but does not determine how its penalty sh...
The paper proposes a multi‑agent reinforcement learning framework that dynamically allocates incentives to drivers, encouraging them to take alternative routes. By doing so, it reduces total travel time by up to 16% and CO₂ emissions by 9% under an unlimited budget, while revealing a trade‑off between the two objectives. The study demonstrates that a carefully weighted multi‑objective function can improve both travel time and emissions beyond baseline levels.
arXiv:2606. 11284v1 Announce Type: cross Abstract: Real-world multi-agent systems, from traffic coordination to resource allocation, are often modeled as general-sum games where individual incentives conflict with collective welfare.
arXiv:2608. 12363v1 Announce Type: cross Abstract: European countries are debating policies to mitigate the increased energy costs caused by renewed geopolitical tensions, while pursuing decarbonization and electrification.
The paper presents a framework that links data‑center electricity demand growth, available generation capacity, and market‑clearing prices to explain rising electricity costs. It first uses a deterministic model to show how varying demand and supply growth estimates influence prices, then extends to stochastic processes that generate probabilistic distributions for supply, demand, and prices. Finally, it formulates generation expansion as a stochastic control problem, illustrating how uncertainties in load forecasts, development risks, and potential overbuilding can dampen investment incentives needed to stabilize prices.