Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning

In this paper, we propose a new learning technique named message-dropout to improve the performance for multi-agent deep reinforcement learning under two application scenarios: 1) classical multi-agent reinforcement learning with direct message communication among agents and 2) centralized training with decentralized execution. In the first application scenario of multi-agent systems in which direct message communication among agents is allowed, the message-dropout technique drops out the received messages from other agents in a block-wise manner with a certain probability in the training phase and compensates for this effect by multiplying the weights of the dropped-out block units with a correction probability. The applied message-dropout technique effectively handles the increased input dimension in multi-agent reinforcement learning with communication and makes learning robust against communication errors in the execution phase. In the second application scenario of centralized training with decentralized execution, we particularly consider the application of the proposed message-dropout to Multi-Agent Deep Deterministic Policy Gradient (MADDPG), which uses a centralized critic to train a decentralized actor for each agent. We evaluate the proposed message-dropout technique for several games, and numerical results show that the proposed message-dropout technique with proper dropout rate improves the reinforcement learning performance significantly in terms of the training speed and the steady-state performance in the execution phase.Comment: The 33rd AAAI Conference on Artificial Intelligence (AAAI) 201

Fine Mapping of the SCN Resistance Locus \u3ci\u3erhg1-b\u3c/i\u3e from PI 88788

Soybean cyst nematode (SCN) (Heterodera glycines Ichinohe) is the most economically damaging soybean [Glycine max (L.) Merr.] pest in the USA and genetic resistance is a key component for its control. Although SCN resistance is quantitative, the rhg1 locus on chromosome 18 (formerly known as Linkage Group G) confers a high level of resistance. The objective of this study was to fi ne-map the rhg1-b allele that is derived from plant introduction (PI) 88788. F2 and F3 plants and F3:4 lines from crosses between SCN resistant and susceptible genotypes were tested with genetic markers to identify recombination events close to rhg1-b. Lines developed from these recombinant plants were then tested for resistance to the SCN isolate PA3, which originally had an HG type 0 phenotype, and with genetic markers. Analysis of lines carrying key recombination events positioned rhg1-b between the simple sequence repeat (SSR) markers BARCSOYSSR_18_0090 and BARCSOYSSR_18_0094. This places rhg1-b to a 67-kb region of the ‘Williams 82’ genome sequence. The receptor-like kinase gene that has been previously identified as a candidate for the ‘Peking’-derived SCN resistant rhg1 gene is adjacent to, but outside of, the rhg1-b interval defined in the present study

Automated target acquisition and docking RFID system in a cluttered environment

JAIST 21世紀COEシンポジウム2008「検証進化可能電子社会」= JAIST 21st Century COE Symposium 2008 Verifiable and Evolvable e-Society, 開催：2008年3月3日～4日, 開催場所：北陸先端科学技術大学院大学GRP研究員発表会　セッションA-3発表資

Direction Sensing RFID Reader for Mobile Robot Navigation

A self-contained direction sensing radio frequency identification (RFID) reader is developed employing a dual-directional antenna for automated target acquisition and docking of a mobile robot in indoor environments. The dual-directional antenna estimates the direction of arrival (DOA) of signals from a transponder by using the ratio of the received signal strengths between two adjacent antennas. This enables the robot to continuously monitor the changes in transponder directions and ensures reliable docking guidance to the target transponder. One of the technical challenges associated with this RFID direction finding is to sustain the accuracy of the estimated DOA that varies according to environmental conditions. It is often the case that the robot loses its way to the target in a cluttered environment. To cope with this problem, the direction correction algorithm is proposed to triangulate the location of the transponder with the most recent three DOA estimates. Theoretical simulation results verify the reliability of the proposed algorithm that quantifies the potential error in the DOA estimation. Using the algorithm, we validate mobile robot docking to an RFID transponder in an office environment occupied by obstacles

RFID-based mobile robot guidance to a stationary target

Retrieving accurate location information about an object in real-time, as well as any general information pertinent to the object, is a key to enabling a robot to perform a task in cluttered, dynamically changing environment. In this paper, we address a novel technique for the guidance of mobile robots to help them identify, locate, and approach a target in our daily environments. To this end, we propose a standard for the use of radio-frequency identification (RFID) systems and develop a prototype that can be easily installed in existing mobile robots. Specifically, when an RF signal is transmitted from an RF transponder, the proposed RFID system reads the transponder-encoded data and simultaneously picks up the direction of the transponder using the received signal strength pattern. Based on the angle of signal arrival, we develop the guidance strategies that enable a robot to find its way to the transponder position. Moreover, to cope with multi-path reflection and unexpected distortions of the signals resulted from environmental effects, we present several algorithms for reconstructing the signals. We demonstrate that an off-the-self mobile robot equipped with the proposed system locates and approaches a stationary target object. Experimental results show that the accuracy of the proposed system operating at a frequency of 315 MHz falls within a reasonable range in our normal office environment

Automated Robot Docking Using Direction Sensing RFID

Automated target acquisition and docking is key to enabling various applications of autonomous mobile robots in indoor environments. For the purpose, many researches have been devoted to the development of location sensing techniques employing the latest in RFID or GPS. However, it has not yet become possible to attain high accuracy in those techniques, particularly in cluttered or dynamically changing environments. In this paper, we propose a novel location sensing RFID reader equipped with a dual directional antenna that communicates with controllable RF transponders. The dual directional antenna estimates the direction of arrival (DOA) of signals from various transponders by using the ratio of the received strength between two antennas. This enables the robot to continuously monitor the changes in the ratio and find its way to the target transponder. To verify the validity of the proposed system in real environments populated with unknown obstacles, we perform detailed experiments using simulations and hardware implementations. Specifically, the target acquisition and docking guidance are demonstrated in a multiple transponder environment under various circumstances

Fine Mapping of the SCN Resistance Locus \u3ci\u3erhg1-b\u3c/i\u3e from PI 88788

Soybean cyst nematode (SCN) (Heterodera glycines Ichinohe) is the most economically damaging soybean [Glycine max (L.) Merr.] pest in the USA and genetic resistance is a key component for its control. Although SCN resistance is quantitative, the rhg1 locus on chromosome 18 (formerly known as Linkage Group G) confers a high level of resistance. The objective of this study was to fi ne-map the rhg1-b allele that is derived from plant introduction (PI) 88788. F2 and F3 plants and F3:4 lines from crosses between SCN resistant and susceptible genotypes were tested with genetic markers to identify recombination events close to rhg1-b. Lines developed from these recombinant plants were then tested for resistance to the SCN isolate PA3, which originally had an HG type 0 phenotype, and with genetic markers. Analysis of lines carrying key recombination events positioned rhg1-b between the simple sequence repeat (SSR) markers BARCSOYSSR_18_0090 and BARCSOYSSR_18_0094. This places rhg1-b to a 67-kb region of the ‘Williams 82’ genome sequence. The receptor-like kinase gene that has been previously identified as a candidate for the ‘Peking’-derived SCN resistant rhg1 gene is adjacent to, but outside of, the rhg1-b interval defined in the present study