Domain-adapted Learning and Imitation: DRL for Power Arbitrage
University College London（伦敦大学学院）；Shell Global Solutions International（壳牌全球解决方案国际公司）；Shell Shared Service Center Chennai（壳牌金奈共享服务中心）；Shell Energy North America（壳牌能源北美公司）
Yuanrong Wang, Vignesh Raja Swaminathan, Nikita P. Granger, Carlos Ros Perez, Christian Michler
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.
Imitation and Adaptation Based on Consistency: A Quadruped Robot Imitates Animals from Videos Using Deep Reinforcement Learning
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造创新研究院）；Westlake University（西湖大学）；University of Chinese Academy of Sciences（中国科学院大学）
Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving
Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science（印度科学学院罗伯特·博世信息物理系统中心）；Indian Institute of Science（印度科学学院）；PES university（PES大学）
Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach
Fraunhofer Institute for Energy Economics and Energy System Technology (IEE)（弗劳恩霍夫能源经济与能源系统技术研究所）；Intelligent Embedded Systems, University of Kassel（卡塞尔大学智能嵌入式系统系）；TenneT TSO B.V.（TenneT TSO公司）
Deep Reinforcement Learning for Autonomous Driving: A Survey
Navya（纳维亚）；Valeo（法雷奥）；ENSTA Paris, Institut Polytechnique de Paris（巴黎理工学院巴黎高等先进技术学院）；AKKA Technologies（AKKA科技公司）；National University of Ireland, Galway（爱尔兰国立高威大学）
