Search Results for author:
Found 6 papers, 3 papers with code
Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning
In detail, we theoretically demonstrate that the effects of non-stationarity introduced by multiple timescales can be learned by a periodic multi-agent policy.
Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMC
We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence.
From Model-Based to Model-Free: Learning Building Control for Demand Response
We demonstrate, in this context, that hybrid methods offer many benefits over both purely model-free and model-based methods as long as certain requirements are met.
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems
We present the PowerGridworld software package to provide users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environments that readily integrate with existing training frameworks for reinforcement learning (RL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem
To help bridge the gap between novel and existing methods, we propose a modular framework for fleet rebalancing based on model-free reinforcement learning (RL) that can leverage an existing dispatch method to minimize system cost.
Learning-Accelerated ADMM for Distributed Optimal Power Flow
We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions.
