A Dual-Positive Monotone Parameterization for Multi-Segment Bids and a Validity Assessment Framework for Reinforcement Learning Agent-based Simulation of Electricity Markets

2026-04-11

AI Summary

This work addresses critical limitations in existing reinforcement learning agents for simulating monotonic, bounded, multi-step stepwise bidding strategies in electricity markets, where reliance on non-differentiable or non-bijective post-processing mappings induces gradient distortion and spurious convergence. To overcome these issues, the paper proposes a differentiable, bijective, and doubly positive monotonic parameterization that directly generates bidding strategies satisfying both monotonicity and boundedness constraints. Furthermore, it introduces a rigorous evaluation framework grounded in the distance to Nash equilibrium. This approach effectively eliminates gradient distortion, ensures stable convergence, and substantially enhances the credibility and theoretical rigor of market mechanism simulations.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Sampling/Simulation-based Search
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Reinforcement learning agent-based simulation (RL-ABS) has become an important tool for electricity market mechanism analysis and evaluation. In the modeling of monotone, bounded, multi-segment stepwise bids, existing methods typically let the policy network first output an unconstrained action and then convert it into a feasible bid curve satisfying monotonicity and boundedness through post-processing mappings such as sorting, clipping, or projection. However, such post-processing mappings often fail to satisfy continuous differentiability, injectivity, and invertibility at boundaries or kinks, thereby causing gradient distortion and leading to spurious convergence in simulation results. Meanwhile, most existing studies conduct mechanism analysis and evaluation mainly on the basis of training-curve convergence, without rigorously assessing the distance between the simulation outcomes and Nash equilibrium, which severely undermines the credibility of the results. To address these issues, this paper proposes...

Problem

• Reinforcement Learning Agent-based Simulation
• Monotone Bids
• Gradient Distortion
• Nash Equilibrium
• Electricity Markets

Innovation

• Dual-Positive Monotone Parameterization
• Reinforcement Learning Agent-based Simulation
• Electricity Market Bidding
• Differentiable Feasible Action Space
• Nash Equilibrium Validation