A Dual-Positive Monotone Parameterization for Multi-Segment Bids and a Validity Assessment Framework for Reinforcement Learning Agent-based Simulation of Electricity Markets
Summary: arXiv:2604.10252v1 Announce Type: new
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…
Key Contributions
- Introduction of a dual-positive monotone parameterization technique for multi-segment bids.
- Development of a validity assessment framework for RL-ABS in electricity markets.
- Improvement in the differentiability and stability of the bid curves output by reinforcement learning models.
- Rigorous evaluation methods for assessing the simulation outcomes against Nash equilibrium.
Methodology
The proposed methodology enhances the existing RL-ABS frameworks by integrating a dual-positive monotone parameterization. This approach enables the generation of feasible and smooth bid curves that inherently respect the properties of monotonicity and boundedness. By eliminating the reliance on post-processing mappings, the new method ensures:
- Continuous Differentiability: The bid curves exhibit smooth transitions, which is essential for effective gradient-based optimization.
- Injectivity and Invertibility: The parameterization guarantees unique bid representations, reducing the risk of ambiguous outputs.
- Accurate Gradient Calculation: With a well-structured bid curve, the gradient calculations during training are more reliable, leading to improved convergence properties.
Validity Assessment Framework
In addition to the technical advancements in bid parameterization, the paper introduces a robust validity assessment framework. This framework aims to bridge the gap between simulated outcomes and theoretical benchmarks, specifically Nash equilibrium. The framework encompasses:
- Metrics for Outcome Evaluation: Establishing quantitative measures to evaluate the proximity of simulation results to Nash equilibrium.
- Statistical Analysis: Employing statistical methods to analyze the reliability of simulation results.
- Scenario Testing: Conducting extensive scenario testing to validate the robustness of the proposed method under varying market conditions.
Conclusion
This paper presents a significant advancement in the field of RL-ABS for electricity market analysis. The dual-positive monotone parameterization coupled with a validity assessment framework not only enhances the quality of bid representations but also strengthens the credibility of simulation results. Future work will focus on further refining the model and expanding its application across different market structures.