mlsanityData to 14 Oct 2026

A proximal policy optimization based intelligent home solar management

arXiv 2404.03888 ↗First posted 5 Apr 2024cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

In the smart grid, the prosumers can sell unused electricity back to the power grid, assuming the prosumers own renewable energy sources and storage units. The maximizing of their profits under a dynamic electricity market is a problem that requires intelligent planning. To address this, we propose a framework based on Proximal Policy Optimization (PPO) using recurrent rewards. By using the information about the rewards modeled effectively with PPO to maximize our objective, we were able to get over 30% improvement over the other naive algorithms in accumulating total profits. This shows promise in getting reinforcement learning algorithms to perform tasks required to plan their actions in complex domains like financial markets. We also introduce a novel method for embedding longs based on soliton waves that outperformed normal embedding in our use case with random floating point data augmentation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 17 references that are in the map. First posted on arXiv 5 Apr 2024.

Builds on

5 of 17 references.

References · 5

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  3. 2024Amortized Planning with Large-Scale Transformers: A Case Study on Chesscited by 49
  4. 2016Learning to Perform Physics Experiments via Deep Reinforcement Learningcited by 47
  5. 2017Managing Price Uncertainty in Prosumer-Centric Energy Trading: A Prospect-Theoretic Stackelberg Game Approachcited by 4

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  1. 2025Deep Reinforcement Learning-Based Bidding Strategies for Prosumers Trading in Double Auction-Based Transactive Energy Market
  2. 2025A Reinforcement Learning Approach for Optimal Control in Microgrids
  3. 2025Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks
  4. 2025Optimizing Day-Ahead Energy Trading with Proximal Policy Optimization and Blockchain
  5. 2024Optimal Control of Renewable Energy Communities subject to Network Peak Fees with Model Predictive Control and Reinforcement Learning Algorithms
  6. 2025Assessing Long-Term Electricity Market Design for Ambitious Decarbonization Targets using Multi-Agent Reinforcement Learning
  7. 2024Explainable Reinforcement Learning-based Home Energy Management Systems using Differentiable Decision Trees
  8. 2024Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration