Decentralized Coordination of Distributed Energy Resources through Local Energy Markets and Deep Reinforcement Learning

no code implementations • 19 Apr 2024 • Daniel May, Matthew Taylor, Petr Musilek

Existing studies at the intersection of transactive energy and model-free control primarily focus on socioeconomic and self-consumption metrics, overlooking the crucial goal of reducing community-level net load variability.

Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction

no code implementations • 2 Sep 2022 • Taher Jafferjee, Juliusz Ziomek, Tianpei Yang, Zipeng Dai, Jianhong Wang, Matthew Taylor, Kun Shao, Jun Wang, David Mguni

Centralised training with decentralised execution (CT-DE) serves as the foundation of many leading multi-agent reinforcement learning (MARL) algorithms.

Multi-agent Reinforcement Learning reinforcement-learning +3

Learning to Shape Rewards using a Game of Two Partners

no code implementations • 16 Mar 2021 • David Mguni, Taher Jafferjee, Jianhong Wang, Nicolas Perez-Nieves, Tianpei Yang, Matthew Taylor, Wenbin Song, Feifei Tong, Hui Chen, Jiangcheng Zhu, Jun Wang, Yaodong Yang

Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards.

reinforcement-learning Reinforcement Learning (RL) +1

Towards Cooperation in Sequential Prisoner's Dilemmas: a Deep Multiagent Reinforcement Learning Approach

no code implementations • 1 Mar 2018 • Weixun Wang, Jianye Hao, Yixi Wang, Matthew Taylor

We introduce a Sequential Prisoner's Dilemma (SPD) game to better capture the aforementioned characteristics.

reinforcement-learning Reinforcement Learning (RL)

Using Language Groundings for Context-Sensitive Text Prediction

no code implementations • WS 2016 • Timothy Lewis, Cynthia Matuszek, Amy Hurst, Matthew Taylor

Language Modelling Text Generation

Using PCA to Efficiently Represent State Spaces

no code implementations • 2 May 2015 • William Curran, Tim Brys, Matthew Taylor, William Smart

When using dimensionality reduction in Mario, learning converges much faster to a good policy.

Benchmarking Dimensionality Reduction