Distributed Actor-Critic Algorithms for Multiagent Reinforcement Learning Over Directed Graphs

Actor-critic (AC) cooperative multiagent reinforcement learning (MARL) over directed graphs is studied in this article. The goal of the agents in MARL is to maximize the globally averaged return in a distributed way, i.e., each agent can only exchange information with its neighboring agents. AC methods proposed in the literature require the...

Planning Jerk-Optimized Trajectory with Discrete Time Constraints for Redundant Robots

We present a method for effectively planning the motion trajectory of robots in manufacturing tasks, the tool paths of which are usually complex and have a large number of discrete time constraints as waypoints. Kinematic redundancy also exists in these robotic systems. The jerk of motion is optimized in our trajectory planning method at the...

Distributed Reinforcement Learning Algorithm for Dynamic Economic Dispatch with Unknown Generation Cost Functions

In this article, the dynamic economic dispatch (DED) problem for smart grid is solved under the assumption that no knowledge of the mathematical formulation of the actual generation cost functions is available. The objective of the DED problem is to find the optimal power output of each unit at each time so as to minimize the total generation...

Steering Micro-Robotic Swarm by Dynamic Actuating Fields

We present a general solution for steering microrobotic<br/>swarm by dynamic actuating fields. In our approach, the<br/>motion of micro-robots is controlled by changing the actuating<br/>direction of a field applied to them. The time-series sequence<br/>of actuating field’s directions can be computed automatically.<br/>Given a target position in...