arXiv subjects

Jingjie Ni

Publications and source records attributed to Jingjie Ni.

4 recordsLinked to original sources

arXiv2025

Reinforcement Learning based Constrained Optimal Control: an Interpretable Reward Design

This paper presents an interpretable reward design framework for reinforcement learning based constrained optimal control problems with state and terminal constraints. The problem is formalized within a standard partially observable Markov decision process framework. The reward function is constructed from four weighted components: a terminal constraint reward, a guidance reward, a penalty for state constraint violations, and a cost reduction incentive reward. A theoretically justified reward design is then presented, which establishes bounds on the weights of the components. This approach ensures that constraints are satisfied and objectives are optimized while mitigating numerical instability. Acknowledging the importance of prior knowledge in reward design, we sequentially solve two subproblems, using each solution to inform the reward design for the subsequent problem. Subsequently, we integrate reinforcement learning with curriculum learning, utilizing policies derived from simpler subproblems to assist in tackling more complex challenges, thereby facilitating convergence. The framework is evaluated against original and randomly weighted reward designs in a multi-agent particle environment. Experimental results demonstrate that the proposed approach significantly enhances satisfaction of terminal and state constraints and optimization of control cost.

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arXiv2024

Reinforcement Learning Based Minimum State-flipped Control for the Reachability of Boolean Control Networks

This paper proposes model-free reinforcement learning methods for minimum-cost state-flipped control in Boolean control networks (BCNs). We tackle two questions: 1) finding the flipping kernel, namely the flip set with the smallest cardinality ensuring reachability, and 2) deriving optimal policies to minimize the number of flipping actions for reachability based on the obtained flipping kernel. For question 1), Q-learning's capability in determining reachability is demonstrated. To expedite convergence, we incorporate two improvements: i) demonstrating that previously reachable states remain reachable after adding elements to the flip set, followed by employing transfer learning, and ii) initiating each episode with special initial states whose reachability to the target state set are currently unknown. Question 2) requires optimal control with terminal constraints, while Q-learning only handles unconstrained problems. To bridge the gap, we propose a BCN-characteristics-based reward scheme and prove its optimality. Questions 1) and 2) with large-scale BCNs are addressed by employing small memory Q-learning, which reduces memory usage by only recording visited action-values. An upper bound on memory usage is provided to assess the algorithm's feasibility. To expedite convergence for question 2) in large-scale BCNs, we introduce adaptive variable rewards based on the known maximum steps needed to reach the target state set without cycles. Finally, the effectiveness of the proposed methods is validated on both small- and large-scale BCNs.

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arXiv2023

Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks

In this paper, we investigate the problem of controlling probabilistic Boolean control networks (PBCNs) to achieve reachability with maximum probability in the finite time horizon. We address three questions: 1) finding control policies that achieve reachability with maximum probability under fixed, and particularly, varied finite time horizon, 2) leveraging prior knowledge to solve question 1) with faster convergence speed in scenarios where time is a variable framework, and 3) proposing an enhanced Q-learning (QL) method to efficiently address the aforementioned questions for large-scale PBCNs. For question 1), we demonstrate the applicability of QL method on the finite-time reachability problem. For question 2), considering the possibility of varied time frames, we incorporate transfer learning (TL) technique to leverage prior knowledge and enhance convergence speed. For question 3), an enhanced model-free QL approach that improves upon the traditional QL algorithm by introducing memory-efficient modifications to address these issues in large-scale PBCNs effectively. Finally, we apply the proposed method to two examples: a small-scale PBCN and a large-scale PBCN, demonstrating the effectiveness of our approach.

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arXiv2023

Deep Reinforcement Learning Based Optimal Infinite-Horizon Control of Probabilistic Boolean Control Networks

In this paper, a deep reinforcement learning based method is proposed to obtain optimal policies for optimal infinite-horizon control of probabilistic Boolean control networks (PBCNs). Compared with the existing literatures, the proposed method is model-free, namely, the system model and the initial states needn't to be known. Meanwhile, it is suitable for large-scale PBCNs. First, we establish the connection between deep reinforcement learning and optimal infinite-horizon control, and structure the problem into the framework of the Markov decision process. Then, PBCNs are defined as large-scale or small-scale, depending on whether the memory of the action-values exceeds the RAM of the computer. Based on the newly introduced definition, Q-learning (QL) and double deep Q-network (DDQN) are applied to the optimal infinite-horizon control of small-scale and large-scale PBCNs, respectively. Meanwhile, the optimal state feedback controllers are designed. Finally, two examples are presented, which are a small-scale PBCN with 3 nodes, and a large-scale one with 28 nodes. To verify the convergence of QL and DDQN, the optimal control policy and the optimal action-values, which are obtained from both the algorithms, are compared with the ones based on a model-based method named policy iteration. Meanwhile, the performance of QL is compared with DDQN in the small-scale PBCN.

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