Dijun Luo

AAAI Conference 2022 Conference Paper

iGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control

• Xiaoyan Cao
• Yao Yao
• Lanqing Li
• WanPeng Zhang
• Zhicheng An
• Zhong Zhang
• Li Xiao
• Shihui Guo

Agriculture is the foundation of human civilization. However, the rapid increase of the global population poses a challenge on this cornerstone by demanding more food. Modern autonomous greenhouses, equipped with sensors and actuators, provide a promising solution to the problem by empowering precise control for high-efficient food production. However, the optimal control of autonomous greenhouses is challenging, requiring decision-making based on high-dimensional sensory data, and the scaling of production is limited by the scarcity of labor capable of handling this task. With the advances of artificial intelligence (AI), the internet of things (IoT), and cloud computing technologies, we are hopeful to provide a solution to automate and smarten greenhouse control to address the above challenges. In this paper, we propose a smart agriculture solution named iGrow, for autonomous greenhouse control (AGC): (1) for the first time, we formulate the AGC problem as a Markov decision process (MDP) optimization problem; (2) we design a neural network-based simulator incorporated with the incremental mechanism to simulate the complete planting process of an autonomous greenhouse, which provides a testbed for the optimization of control strategies; (3) we propose a closed-loop bi-level optimization algorithm, which can dynamically re-optimize the greenhouse control strategy with newly observed data during real-world production. We not only conduct simulation experiments but also deploy iGrow in real scenarios, and experimental results demonstrate the effectiveness and superiority of iGrow in autonomous greenhouse simulation and optimal control. Particularly, compelling results from the tomato pilot project in real autonomous greenhouses show that our solution significantly increases crop yield (+10. 15%) and net profit (+92. 70%) with statistical significance compared to planting experts. Our solution opens up a new avenue for greenhouse production. The code is available at https: //github. com/holmescao/iGrow. git.

ICAPS Conference 2021 Conference Paper

A Simulator-based Planning Framework for Optimizing Autonomous Greenhouse Control Strategy

• Zhicheng An
• Xiaoyan Cao
• Yao Yao 0006
• Wanpeng Zhang 0002
• Lanqing Li
• Yue Wang
• Shihui Guo
• Dijun Luo

The rapidly growing global population presents challenges and demands for efficient production of healthy fresh food. Autonomous greenhouse equipped with standard sensors and actuators (such as heating and lighting) which enables control of indoor climate for crop production, contributes to producing higher yields. However, it requires skilled and expensive labor, as well as a large amount of energy. An autonomous greenhouse control strategy, powered by AI algorithms by optimizing the yields and resource use simultaneously, offers an ideal solution to the dilemma. In this paper, we propose a two-stage planning framework to automatically optimize greenhouse control setpoints given specific outside weather conditions. Firstly, we take advantage of cumulative planting data and horticulture knowledge to build a multi-modular simulator using neural networks, to simulate climate change and crop growth in the greenhouse. Secondly, two AI algorithms (reinforcement learning and heuristic algorithm) as planning methods are applied to obtain optimal control strategies based on the simulator. We evaluate our framework on a cherry-tomato planting dataset and demonstrate that the simulator is able to simulate greenhouse planting processes with high accuracy and fast speed. Moreover, the control strategies produced by the AI algorithms all obtain superhuman performance, in particular, significantly outperform all teams of the second “Autonomous Greenhouse Challenge” in terms of net profits.

ICLR Conference 2021 Conference Paper

FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization

• Lanqing Li
• Rui Yang
• Dijun Luo

We study the offline meta-reinforcement learning (OMRL) problem, a paradigm which enables reinforcement learning (RL) algorithms to quickly adapt to unseen tasks without any interactions with the environments, making RL truly practical in many real-world applications. This problem is still not fully understood, for which two major challenges need to be addressed. First, offline RL usually suffers from bootstrapping errors of out-of-distribution state-actions which leads to divergence of value functions. Second, meta-RL requires efficient and robust task inference learned jointly with control policy. In this work, we enforce behavior regularization on learned policy as a general approach to offline RL, combined with a deterministic context encoder for efficient task inference. We propose a novel negative-power distance metric on bounded context embedding space, whose gradients propagation is detached from the Bellman backup. We provide analysis and insight showing that some simple design choices can yield substantial improvements over recent approaches involving meta-RL and distance metric learning. To the best of our knowledge, our method is the first model-free and end-to-end OMRL algorithm, which is computationally efficient and demonstrated to outperform prior algorithms on several meta-RL benchmarks.

ICRA Conference 2021 Conference Paper

Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation

• Yao Yao 0006
• Li Xiao 0006
• Zhicheng An
• Wanpeng Zhang 0002
• Dijun Luo

Model-based deep reinforcement learning has achieved success in various domains that require high sample efficiencies, such as Go and robotics. However, there are some remaining issues, such as planning efficient explorations to learn more accurate dynamic models, evaluating the uncertainty of the learned models, and more rational utilization of models. To mitigate these issues, we present MEEE, a model-ensemble method that consists of optimistic exploration and weighted exploitation. During exploration, unlike prior methods directly selecting the optimal action that maximizes the expected accumulative return, our agent first generates a set of action candidates and then seeks out the optimal action that takes both expected return and future observation novelty into account. During exploitation, different discounted weights are assigned to imagined transition tuples according to their model uncertainty respectively, which will prevent model predictive error propagation in agent training. Experiments on several challenging continuous control benchmark tasks demonstrated that our approach outperforms other model-free and model-based state-of-the-art methods, especially in sample complexity.

NeurIPS Conference 2012 Conference Paper

Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach

• Dijun Luo
• Heng Huang
• Feiping Nie
• Chris Ding

In many graph-based machine learning and data mining approaches, the quality of the graph is critical. However, in real-world applications, especially in semi-supervised learning and unsupervised learning, the evaluation of the quality of a graph is often expensive and sometimes even impossible, due the cost or the unavailability of ground truth. In this paper, we proposed a robust approach with convex optimization to ``forge'' a graph: with an input of a graph, to learn a graph with higher quality. Our major concern is that an ideal graph shall satisfy all the following constraints: non-negative, symmetric, low rank, and positive semidefinite. We develop a graph learning algorithm by solving a convex optimization problem and further develop an efficient optimization to obtain global optimal solutions with theoretical guarantees. With only one non-sensitive parameter, our method is shown by experimental results to be robust and achieve higher accuracy in semi-supervised learning and clustering under various settings. As a preprocessing of graphs, our method has a wide range of potential applications machine learning and data mining.

IJCAI Conference 2011 Conference Paper

Ball Ranking Machine for Content-Based Multimedia Retrieval

• Dijun Luo
• Heng Huang

In this paper, we propose the new Ball Ranking Machines (BRMs) to address the supervised ranking problems. In previous work, supervised ranking methods have been successfully applied in various information retrieval tasks. Among these methodologies, the Ranking Support Vector Machines (Rank SVMs) are well investigated. However, one major fact limiting their applications is that Ranking SVMs need optimize a margin-based objective function over all possible document pairs within all queries on the training set. In consequence, Ranking SVMs need select a large number of support vectors among a huge number of support vector candidates. This paper introduces a new model of of Ranking SVMs and develops an efficient approximation algorithm, which decreases the training time and generates much fewer support vectors. Empirical studies on synthetic data and content-based image/video retrieval data show that our method is comparable to Ranking SVMs in accuracy, but use much fewer ranking support vectors and significantly less training time.

ICML Conference 2011 Conference Paper

Cauchy Graph Embedding

• Dijun Luo
• Chris H. Q. Ding
• Feiping Nie 0001
• Heng Huang 0001

IJCAI Conference 2011 Conference Paper

Cluster Indicator Decomposition for Efficient Matrix Factorization

• Dijun Luo
• Chris Ding
• Heng Huang

We propose a new clustering based low-rank matrix approximation method, Cluster Indicator Decomposition (CID), which yields more accurate low-rank approximations than previous commonly used singular value decomposition and other Nyströ m style decompositions. Our model utilizes the intrinsic structures of data and theoretically be more compact and accurate than the traditional low rank approximation approaches. The reconstruction in CID is extremely fast leading to a desirable advantage of our method in large-scale kernel machines (like Support Vector Machines) in which the reconstruction of the kernels needs to be frequently computed. Experimental results indicate that our approach compress images much more efficiently than other factorization based methods. We show that combining our method with Support Vector Machines obtains more accurate approximation and more accurate prediction while consuming much less computation resources.

AAAI Conference 2011 Conference Paper

Linear Discriminant Analysis: New Formulations and Overfit Analysis

• Dijun Luo
• Chris Ding
• Heng Huang

In this paper, we will present a unified view for LDA. We will (1) emphasize that standard LDA solutions are not unique, (2) propose several new LDA formulations: St-orthonormal LDA, Sw-orthonormal LDA and orthogonal LDA which have unique solutions, and (3) show that with St-orthonormal LDA and Sw-orthonormal LDA formulations, solutions to all four major LDA objective functions are identical. Furthermore, we perform an indepth analysis to show that the LDA sometimes performs poorly due to over-fitting, i. e. , it picks up PCA dimensions with small eigenvalues. From this analysis, we propose a stable LDA which uses PCA first to reduce to a small PCA subspace and do LDA in the subspace.

AAAI Conference 2011 Conference Paper

Multi-Level Cluster Indicator Decompositions of Matrices and Tensors

• Dijun Luo
• Chris Ding
• Heng Huang

A main challenging problem for many machine learning and data mining applications is that the amount of data and features are very large, so that low-rank approximations of original data are often required for efficient computation. We propose new multi-level clustering based low-rank matrix approximations which are comparable and even more compact than Singular Value Decomposition (SVD). We utilize the cluster indicators of data clustering results to form the subspaces, hence our decomposition results are more interpretable. We further generalize our clustering based matrix decompositions to tensor decompositions that are useful in high-order data analysis. We also provide an upper bound for the approximation error of our tensor decomposition algorithm. In all experimental results, our methods significantly outperform traditional decomposition methods such as SVD and high-order SVD.