Multirobot Collaborative Pursuit Target Robot by Improved MADDPG

Abstract

Policy formulation is one of the main problems in multirobot systems, especially in multirobot pursuit-evasion scenarios, where both sparse rewards and random environment changes bring great difficulties to find better strategy. Existing multirobot decision-making methods mostly use environmental rewards to promote robots to complete the target task that cannot achieve good results. This paper proposes a multirobot pursuit method based on improved multiagent deep deterministic policy gradient (MADDPG), which solves the problem of sparse rewards in multirobot pursuit-evasion scenarios by combining the intrinsic reward and the external environment. The state similarity module based on the threshold constraint is as a part of the intrinsic reward signal output by the intrinsic curiosity module, which is used to balance overexploration and insufficient exploration, so that the agent can use the intrinsic reward more effectively to learn better strategies. The simulation experiment results show that the proposed method can improve the reward value of robots and the success rate of the pursuit task significantly. The intuitive change is obviously reflected in the real-time distance between the pursuer and the escapee, the pursuer using the improved algorithm for training can get closer to the escapee more quickly, and the average following distance also decreases.

Random curiosity-driven exploration in deep reinforcement learning

“…Below we consider a function h which quantifies the relationship between group A and group B where the agents in group A are the opponents of the agents in group B. Similar to Zhou et al (2022), the external reward signal is composed of the relative external reward and the base external reward. The relative external reward is calculated as:…”

Multi-Agent Collaborative Target Search Based on the Multi-Agent Deep Deterministic Policy Gradient with Emotional Intrinsic Motivation

“…Through communication, cooperation, or competition between agents, the multi-agent system can complete a large number of complex tasks that cannot be completed by a single agent [1]. Multi-agent collaborative control, with its advantages of high efficiency, high fault tolerance, and inherent parallelism [2], has been widely used in formation [3], unmanned systems [4], network resource allocation [5], multi-robot cooperative motion planning [6], target search [7], and other fields.…”

“…Deep reinforcement learning has been a hot topic in multi-agent target search in recent years [7,[15][16][17][18]. DRL combines the perception ability of deep learning and the decisionmaking ability of reinforcement learning [19], and it provides a solution for the perception and decision-making problems of complex multi-agent systems.…”

“…Another way to make the sparse reward is adding goals, uncertainty measures, or intrinsic motivation inside the deep reinforcement learning exploration. Compared with adding the goal and uncertainty measures, the deep reinforcement learning method based on intrinsic motivation [7,[24][25][26] formalizes a variety of heuristic concepts derived from cognitive psychology into intrinsic reward signals to drive the agent to independently and efficiently explore the environment. On the other hand, the internal reward system and motivation are believed to act to differentiate an intelligent being from an unintelligent one [27].…”

Retracted: Multirobot Collaborative Pursuit Target Robot by Improved MADDPG

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”