Deep Reinforcement Learning for Control of Microgrids: A Review

A microgrid is widely accepted as a prominent solution to enhance resilience and performance in distributed power systems. Microgrids are flexible for adding distributed energy resources in the ecosystem of the electrical networks. Control techniques are used to synchronize distributed energy resources (DERs) due to their turbulent nature. DERs including alternating current, direct current and hybrid load with storage systems have been used in microgrids quite frequently due to which controlling the flow of energy in microgrids have been complex task with traditional control approaches. Distributed as well central approach to apply control algorithms is well-known methods to regulate frequency and voltage in microgrids. Recently techniques based of artificial intelligence are being applied for the problems that arise in operation and control of latest generation microgrids and smart grids. Such techniques are categorized in machine learning and deep learning in broader terms. The objective of this research is to survey the latest strategies of control in microgrids using the deep reinforcement learning approach (DRL). Other techniques of artificial intelligence had already been reviewed extensively but the use of DRL has increased in the past couple of years. To bridge the gap for the researchers, this survey paper is being presented with a focus on only Microgrids control DRL techniques for voltage control and frequency regulation with distributed, cooperative and multi agent approaches are presented in this research

Deep Reinforcement Learning for Swarm Systems

Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to represent the information content required for decentralized decision making. However, concatenation scales poorly to swarm systems with a large number of homogeneous agents as it does not exploit the fundamental properties inherent to these systems: (i) the agents in the swarm are interchangeable and (ii) the exact number of agents in the swarm is irrelevant. Therefore, we propose a new state representation for deep multi-agent RL based on mean embeddings of distributions. We treat the agents as samples of a distribution and use the empirical mean embedding as input for a decentralized policy. We define different feature spaces of the mean embedding using histograms, radial basis functions and a neural network learned end-to-end. We evaluate the representation on two well known problems from the swarm literature (rendezvous and pursuit evasion), in a globally and locally observable setup. For the local setup we furthermore introduce simple communication protocols. Of all approaches, the mean embedding representation using neural network features enables the richest information exchange between neighboring agents facilitating the development of more complex collective strategies.Comment: 31 pages, 12 figures, version 3 (published in JMLR Volume 20

QDQD-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations

The paper considers a class of multi-agent Markov decision processes (MDPs), in which the network agents respond differently (as manifested by the instantaneous one-stage random costs) to a global controlled state and the control actions of a remote controller. The paper investigates a distributed reinforcement learning setup with no prior information on the global state transition and local agent cost statistics. Specifically, with the agents' objective consisting of minimizing a network-averaged infinite horizon discounted cost, the paper proposes a distributed version of $Q$-learning, $\mathcal{QD}$-learning, in which the network agents collaborate by means of local processing and mutual information exchange over a sparse (possibly stochastic) communication network to achieve the network goal. Under the assumption that each agent is only aware of its local online cost data and the inter-agent communication network is \emph{weakly} connected, the proposed distributed scheme is almost surely (a.s.) shown to yield asymptotically the desired value function and the optimal stationary control policy at each network agent. The analytical techniques developed in the paper to address the mixed time-scale stochastic dynamics of the \emph{consensus + innovations} form, which arise as a result of the proposed interactive distributed scheme, are of independent interest.Comment: Submitted to the IEEE Transactions on Signal Processing, 33 page

Guided Deep Reinforcement Learning for Swarm Systems

In this paper, we investigate how to learn to control a group of cooperative agents with limited sensing capabilities such as robot swarms. The agents have only very basic sensor capabilities, yet in a group they can accomplish sophisticated tasks, such as distributed assembly or search and rescue tasks. Learning a policy for a group of agents is difficult due to distributed partial observability of the state. Here, we follow a guided approach where a critic has central access to the global state during learning, which simplifies the policy evaluation problem from a reinforcement learning point of view. For example, we can get the positions of all robots of the swarm using a camera image of a scene. This camera image is only available to the critic and not to the control policies of the robots. We follow an actor-critic approach, where the actors base their decisions only on locally sensed information. In contrast, the critic is learned based on the true global state. Our algorithm uses deep reinforcement learning to approximate both the Q-function and the policy. The performance of the algorithm is evaluated on two tasks with simple simulated 2D agents: 1) finding and maintaining a certain distance to each others and 2) locating a target.Comment: 15 pages, 8 figures, accepted at the AAMAS 2017 Autonomous Robots and Multirobot Systems (ARMS) Worksho