Domain-adapted Learning and Imitation: DRL for Power Arbitrage

• URL: http://arxiv.org/abs/2301.08360v3
• Date: Sun, 10 Sep 2023 19:18:30 GMT
• Title: Domain-adapted Learning and Imitation: DRL for Power Arbitrage
• Authors: Yuanrong Wang, Vignesh Raja Swaminathan, Nikita P. Granger, Carlos Ros Perez, Christian Michler
• Abstract summary: We propose a collaborative dual-agent reinforcement learning approach for this bi-level simulation and optimization of European power arbitrage trading. We introduce two new implementations designed to incorporate domain-specific knowledge by imitating the trading behaviours of power traders. Our study demonstrates that by leveraging domain expertise in a general learning problem, the performance can be improved substantially.
• Score: 1.6874375111244329
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we discuss the Dutch power market, which is comprised of a day-ahead market and an intraday balancing market that operates like an auction. Due to fluctuations in power supply and demand, there is often an imbalance that leads to different prices in the two markets, providing an opportunity for arbitrage. To address this issue, we restructure the problem and propose a collaborative dual-agent reinforcement learning approach for this bi-level simulation and optimization of European power arbitrage trading. We also introduce two new implementations designed to incorporate domain-specific knowledge by imitating the trading behaviours of power traders. By utilizing reward engineering to imitate domain expertise, we are able to reform the reward system for the RL agent, which improves convergence during training and enhances overall performance. Additionally, the tranching of orders increases bidding success rates and significantly boosts profit and loss (P&L). Our study demonstrates that by leveraging domain expertise in a general learning problem, the performance can be improved substantially, and the final integrated approach leads to a three-fold improvement in cumulative P&L compared to the original agent. Furthermore, our methodology outperforms the highest benchmark policy by around 50% while maintaining efficient computational performance.

Related papers

• Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution [0.9553307596675155]
  We introduce the Hierarchical Reinforced Trader (HRT), a novel trading strategy employing a bi-level Hierarchical Reinforcement Learning framework. HRT integrates a Proximal Policy Optimization (PPO)-based High-Level Controller (HLC) for strategic stock selection with a Deep Deterministic Policy Gradient (DDPG)-based Low-Level Controller (LLC) tasked with optimizing trade executions to enhance portfolio value.
  arXiv  Detail & Related papers  (2024-10-19T01:29:38Z)
• Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets [13.03742132147551]
  We develop a novel BESS joint bidding strategy that utilizes deep reinforcement learning (DRL) to bid in the spot and contingency frequency control ancillary services markets. Unlike conventional "black-box" DRL model, our approach is more interpretable and provides valuable insights into the temporal bidding behavior of BESS.
  arXiv  Detail & Related papers  (2024-02-29T12:41:54Z)
• An Auction-based Marketplace for Model Trading in Federated Learning [54.79736037670377]
  Federated learning (FL) is increasingly recognized for its efficacy in training models using locally distributed data. We frame FL as a marketplace of models, where clients act as both buyers and sellers. We propose an auction-based solution to ensure proper pricing based on performance gain.
  arXiv  Detail & Related papers  (2024-02-02T07:25:53Z)
• IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making [33.23156884634365]
  Reinforcement Learning technology has achieved remarkable success in quantitative trading. Most existing RL-based market making methods focus on optimizing single-price level strategies. We propose Imitative Market Maker (IMM), a novel RL framework leveraging both knowledge from suboptimal signal-based experts and direct policy interactions.
  arXiv  Detail & Related papers  (2023-08-17T11:04:09Z)
• Mimicking Better by Matching the Approximate Action Distribution [48.95048003354255]
  We introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations. We show that it requires considerable fewer interactions to achieve expert performance, outperforming current state-of-the-art on-policy methods.
  arXiv  Detail & Related papers  (2023-06-16T12:43:47Z)
• Imitate then Transcend: Multi-Agent Optimal Execution with Dual-Window Denoise PPO [13.05016423016994]
  A novel framework for solving the optimal execution and placement problems using reinforcement learning (RL) with imitation was proposed. The RL agents trained from the proposed framework consistently outperformed the industry benchmark time-weighted average price (TWAP) strategy in execution cost.
  arXiv  Detail & Related papers  (2022-06-21T21:25:30Z)
• Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review [7.2090237123481575]
  Reinforcement Learning is an emerging machine learning technique with advantages compared with conventional optimization tools. This paper presents a review of RL applications in deregulated power market operation including bidding and dispatching strategy optimization. Some RL techniques that have great potentiality to be deployed in bidding and dispatching problems are recommended and discussed.
  arXiv  Detail & Related papers  (2022-05-07T08:02:25Z)
• Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market [58.720142291102135]
  This paper focuses precisely on the study of these markets makers strategies from an agent-based perspective. We propose the application of Reinforcement Learning (RL) for the creation of intelligent market markers in simulated stock markets.
  arXiv  Detail & Related papers  (2021-12-08T14:55:21Z)
• Softmax with Regularization: Better Value Estimation in Multi-Agent Reinforcement Learning [72.28520951105207]
  Overestimation in $Q$-learning is an important problem that has been extensively studied in single-agent reinforcement learning. We propose a novel regularization-based update scheme that penalizes large joint action-values deviating from a baseline. We show that our method provides a consistent performance improvement on a set of challenging StarCraft II micromanagement tasks.
  arXiv  Detail & Related papers  (2021-03-22T14:18:39Z)
• Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation [61.317911756566126]
  We propose a Towards Fair Knowledge Transfer framework to handle the fairness challenge in imbalanced cross-domain learning. Specifically, a novel cross-domain mixup generation is exploited to augment the minority source set with target information to enhance fairness. Our model significantly improves over 20% on two benchmarks in terms of the overall accuracy.
  arXiv  Detail & Related papers  (2020-10-23T06:29:09Z)
• A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding [69.37299910149981]
  A key component for the successful renewable energy sources integration is the usage of energy storage. We propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market. An distributed version of the fitted Q algorithm is chosen for solving this problem due to its sample efficiency. Results indicate that the agent converges to a policy that achieves in average higher total revenues than the benchmark strategy.
  arXiv  Detail & Related papers  (2020-04-13T13:50:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.