Hierarchical Reinforcement Learning for Power Network Topology Control

• URL: http://arxiv.org/abs/2311.02129v1
• Date: Fri, 3 Nov 2023 12:33:00 GMT
• Title: Hierarchical Reinforcement Learning for Power Network Topology Control
• Authors: Blazej Manczak and Jan Viebahn and Herke van Hoof
• Abstract summary: 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.
• Score: 22.203574989348773
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning in high-dimensional action spaces is a key challenge in applying reinforcement learning (RL) to real-world systems. In this paper, we study the possibility of controlling power networks using RL methods. Power networks are critical infrastructures that are complex to control. In particular, the combinatorial nature of the action space poses a challenge to both conventional optimizers and learned controllers. Hierarchical reinforcement learning (HRL) represents one approach to address this challenge. More precisely, a HRL framework for power network topology control is proposed. The HRL framework consists of three levels of action abstraction. At the highest level, there is the overall long-term task of power network operation, namely, keeping the power grid state within security constraints at all times, which is decomposed into two temporally extended actions: 'do nothing' versus 'propose a topology change'. At the intermediate level, the action space consists of all controllable substations. Finally, at the lowest level, the action space consists of all configurations of the chosen substation. By employing this HRL framework, several hierarchical power network agents are trained for the IEEE 14-bus network. Whereas at the highest level a purely rule-based policy is still chosen for all agents in this study, at the intermediate level the policy is trained using different state-of-the-art RL algorithms. At the lowest level, either an RL algorithm or a greedy algorithm is used. The performance of the different 3-level agents is compared with standard baseline (RL or greedy) approaches. A key finding is that the 3-level agent that employs RL both at the intermediate and the lowest level outperforms all other agents on the most difficult task. Our code is publicly available.

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)
• RL-GPT: Integrating Reinforcement Learning and Code-as-policy [82.1804241891039]
  We introduce a two-level hierarchical framework, RL-GPT, comprising a slow agent and a fast agent. The slow agent analyzes actions suitable for coding, while the fast agent executes coding tasks. This decomposition effectively focuses each agent on specific tasks, proving highly efficient within our pipeline.
  arXiv  Detail & Related papers  (2024-02-29T16:07:22Z)
• Multi-Agent Reinforcement Learning for Power Grid Topology Optimization [45.74830585715129]
  This paper presents a hierarchical multi-agent reinforcement learning (MARL) framework tailored for expansive action spaces. Experimental results indicate the MARL framework's competitive performance with single-agent RL methods. We also compare different RL algorithms for lower-level agents alongside different policies for higher-order agents.
  arXiv  Detail & Related papers  (2023-10-04T06:37:43Z)
• Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet [48.96004919910818]
  We propose a multi-agent deep reinforcement learning (MADRL) based power control scheme for the HetNet. To promote cooperation among agents, we develop a penalty-based Q learning (PQL) algorithm for MADRL systems. In this way, an agent's policy can be learned by other agents more easily, resulting in a more efficient collaboration process.
  arXiv  Detail & Related papers  (2022-12-15T17:01:56Z)
• 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)
• URLB: Unsupervised Reinforcement Learning Benchmark [82.36060735454647]
  We introduce the Unsupervised Reinforcement Learning Benchmark (URLB) URLB consists of two phases: reward-free pre-training and downstream task adaptation with extrinsic rewards. We provide twelve continuous control tasks from three domains for evaluation and open-source code for eight leading unsupervised RL methods.
  arXiv  Detail & Related papers  (2021-10-28T15:07:01Z)
• Room Clearance with Feudal Hierarchical Reinforcement Learning [2.867517731896504]
  We introduce a new simulation environment, "it", designed as a tool to build scenarios that can drive RL research in a direction useful for military analysis. We focus on an abstracted and simplified room clearance scenario, where a team of blue agents have to make their way through a building and ensure that all rooms are cleared of enemy red agents. We implement a multi-agent version of feudal hierarchical RL that introduces a command hierarchy where a commander at the higher level sends orders to multiple agents at the lower level who simply have to learn to follow these orders. We find that breaking the task down in this way allows us to
  arXiv  Detail & Related papers  (2021-05-24T15:05:58Z)
• Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning [0.0]
  This paper presents a novel hierarchical deep reinforcement learning (DRL) based design for the voltage control of power grids. We exploit the area-wise division structure of the power system to propose a hierarchical DRL design that can be scaled to the larger grid models. We train area-wise decentralized RL agents to compute lower-level policies for the individual areas, and concurrently train a higher-level DRL agent that uses the updates of the lower-level policies to efficiently coordinate the control actions taken by the lower-level agents.
  arXiv  Detail & Related papers  (2021-01-29T21:30:59Z)
• Hierarchical Reinforcement Learning for Relay Selection and Power Optimization in Two-Hop Cooperative Relay Network [7.5377621697101205]
  We study the outage probability minimizing problem subjected to a total transmission power constraint in a two-hop cooperative relay network. We use reinforcement learning (RL) methods to learn strategies for relay selection and power allocation. We propose a hierarchical reinforcement learning (HRL) framework and training algorithm.
  arXiv  Detail & Related papers  (2020-11-10T04:47:41Z)
• SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning [102.78958681141577]
  We present SUNRISE, a simple unified ensemble method, which is compatible with various off-policy deep reinforcement learning algorithms. SUNRISE integrates two key ingredients: (a) ensemble-based weighted Bellman backups, which re-weight target Q-values based on uncertainty estimates from a Q-ensemble, and (b) an inference method that selects actions using the highest upper-confidence bounds for efficient exploration.
  arXiv  Detail & Related papers  (2020-07-09T17:08:44Z)

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.