HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach

• URL: http://arxiv.org/abs/2405.00629v2
• Date: Thu, 23 May 2024 08:42:25 GMT
• Title: HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach
• Authors: Malte Lehna, Clara Holzhüter, Sven Tomforde, Christoph Scholz,
• Abstract summary: We present a search algorithm to find the Target Topologies (TTs) and upgrade our previously developed DRL agent CurriculumAgent (CAgent) to a novel topology agent. We achieve a 25% better median survival time with our TTs included.
• Score: 1.0874597293913013
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the growth of Renewable Energy (RE) generation, the operation of power grids has become increasingly complex. One solution could be automated grid operation, where Deep Reinforcement Learning (DRL) has repeatedly shown significant potential in Learning to Run a Power Network (L2RPN) challenges. However, only individual actions at the substation level have been subjected to topology optimization by most existing DRL algorithms. In contrast, we propose a more holistic approach by proposing specific Target Topologies (TTs) as actions. These topologies are selected based on their robustness. As part of this paper, we present a search algorithm to find the TTs and upgrade our previously developed DRL agent CurriculumAgent (CAgent) to a novel topology agent. We compare the upgrade to the previous CAgent and can increase their L2RPN score significantly by 10%. Further, we achieve a 25% better median survival time with our TTs included. Later analysis shows that almost all TTs are close to the base topology, explaining their robustness

Related papers

• ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL [80.10358123795946]
  We develop a framework for building multi-turn RL algorithms for fine-tuning large language models. Our framework adopts a hierarchical RL approach and runs two RL algorithms in parallel. Empirically, we find that ArCHer significantly improves efficiency and performance on agent tasks.
  arXiv  Detail & Related papers  (2024-02-29T18:45:56Z)
• Active search and coverage using point-cloud reinforcement learning [50.741409008225766]
  This paper presents an end-to-end deep reinforcement learning solution for target search and coverage. We show that deep hierarchical feature learning works for RL and that by using farthest point sampling (FPS) we can reduce the amount of points. We also show that multi-head attention for point-clouds helps to learn the agent faster but converges to the same outcome.
  arXiv  Detail & Related papers  (2023-12-18T18:16:30Z)
• Hierarchical Reinforcement Learning for Power Network Topology Control [22.203574989348773]
  Learning in high-dimensional action spaces is a key challenge in applying reinforcement learning to real-world systems. In this paper, we study the possibility of controlling power networks using RL methods.
  arXiv  Detail & Related papers  (2023-11-03T12:33:00Z)
• Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents [1.8549313085249322]
  Operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy production. It has been shown that Reinforcement Learning is an efficient and reliable approach with considerable potential for automatic grid operation. In this article, we analyse the submitted agent from Binbinchen and provide novel strategies to improve the agent, both for the RL and the rule-based approach.
  arXiv  Detail & Related papers  (2023-04-03T07:34:43Z)
• MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks [63.24965775030673]
  We propose a novel Reinforcement Learning (RL) approach to design generic Congestion Control (CC) algorithms. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch.
  arXiv  Detail & Related papers  (2023-02-02T18:27:20Z)
• On Transforming Reinforcement Learning by Transformer: The Development Trajectory [97.79247023389445]
  Transformer, originally devised for natural language processing, has also attested significant success in computer vision. We group existing developments in two categories: architecture enhancement and trajectory optimization. We examine the main applications of TRL in robotic manipulation, text-based games, navigation and autonomous driving.
  arXiv  Detail & Related papers  (2022-12-29T03:15:59Z)
• Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading [0.19116784879310028]
  This paper describes how domain knowledge of power system operators can be integrated into reinforcement learning frameworks. A curriculum-based approach with reward tuning is incorporated into the training procedure by modifying the environment. A parallel training approach on multiple scenarios is employed to avoid biasing the agent to a few scenarios and make it robust to the natural variability in grid operations.
  arXiv  Detail & Related papers  (2021-12-18T20:32:05Z)
• Improving Generalization of Deep Reinforcement Learning-based TSP Solvers [19.29028564568974]
  We propose a novel approach named MAGIC that includes a deep learning architecture and a DRL training method. Our architecture, which integrates a multilayer perceptron, a graph neural network, and an attention model, defines a policy that sequentially generates a traveling salesman solution. Our training method includes several innovations: (1) we interleave DRL policy updates with local search (using a new local search technique), (2) we use a novel simple baseline, and (3) we apply gradient learning.
  arXiv  Detail & Related papers  (2021-10-06T15:16:19Z)
• RL-DARTS: Differentiable Architecture Search for Reinforcement Learning [62.95469460505922]
  We introduce RL-DARTS, one of the first applications of Differentiable Architecture Search (DARTS) in reinforcement learning (RL) By replacing the image encoder with a DARTS supernet, our search method is sample-efficient, requires minimal extra compute resources, and is also compatible with off-policy and on-policy RL algorithms, needing only minor changes in preexisting code. We show that the supernet gradually learns better cells, leading to alternative architectures which can be highly competitive against manually designed policies, but also verify previous design choices for RL policies.
  arXiv  Detail & Related papers  (2021-06-04T03:08:43Z)
• Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork [73.94896986868146]
  Phase retrieval is an important component in modern computational imaging systems. Recent advances in deep learning have opened up a new possibility for robust and fast PR. We develop a novel framework for deep unfolding to overcome the existing limitations.
  arXiv  Detail & Related papers  (2021-01-12T08:36:23Z)
• Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples [31.31937554018045]
  Deep reinforcement learning (RL) methods require intensive data from the exploration of the environment to achieve satisfactory performance. We propose a framework that enables an RL agent to reason over its exploration process and distill high-level knowledge for effectively guiding its future explorations. Specifically, we propose a novel RL algorithm that learns high-level knowledge in the form of a finite reward automaton by using the L* learning algorithm.
  arXiv  Detail & Related papers  (2020-06-28T21:13:08Z)

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.