Abstract The advancement of Photovoltaic (PV) systems hinges on the precise optimization of their parameters. Among the numerous optimization techniques, the effectiveness of each often rests on their inherent parameters. This research introduces a new methodology, the Reinforcement Learning-based Golden Jackal Optimizer (RL-GJO). This approach uniquely combines reinforcement learning with the Golden Jackal Optimizer to enhance its efficiency and adaptability in handling various optimization problems. Furthermore, the research incorporates an advanced non-linear hunting strategy to optimize the algorithm’s performance. The proposed algorithm is first validated using 29 CEC2017 benchmark test functions and five engineering-constrained design problems. Secondly, rigorous testing on PV parameter estimation benchmark datasets, including the single-diode model, double-diode model, three-diode model, and a representative PV module, was carried out to highlight the superiority of RL-GJO. The results were compelling: the root mean square error values achieved by RL-GJO were markedly lower than those of the original algorithm and other prevalent optimization methods. The synergy between reinforcement learning and GJO in this approach facilitates faster convergence and improved solution quality. This integration not only improves the performance metrics but also ensures a more efficient optimization process, especially in complex PV scenarios. With an average Freidman’s rank test values of 1.564 for numerical and engineering design problems and 1.742 for parameter estimation problems, the proposed RL-GJO is performing better than the original GJO and other peers. The proposed RL-GJO stands out as a reliable tool for PV parameter estimation. By seamlessly combining reinforcement learning with the golden jackal optimizer, it sets a new benchmark in PV optimization, indicating a promising avenue for future research and applications. 

Enhancing photovoltaic parameter estimation: integration of non-linear hunting and reinforcement learning strategies with golden jackal optimizer

Compared with the traditional robots on earth, space robotics present additional difficulties including complicated, multi&amp;ndash;body dynamics (including anti&amp;ndash;resonances absent with earthly robotic systems), space environmental forces and torques, communication delays, and high expense. Pointing accuracy requirements necessitate control algorithms that can accommodate flexible, multi&amp;ndash;body dynamics particularly. The high cost of placing systems in space drives necessarily lightweight systems lacking structural stiffness. Natural frequencies of space robot vibration are often so low, the act of implementing control torques causes structural resonance. Seeking improved performance, this manuscript introduces and compares a dozen options, revealing seventy&amp;ndash;percent reduction in tracking error may be achieved with only ten percent increase in control effort. Prequel work recommended tracking sinusoidal shaped trajectories while structurally filtering the first mode&amp;rsquo;s resonance and anti&amp;ndash;resonance. The future research recommended in that prequel is manifest in this present work which recommends a simpler system of lower order, eliminating the notch compensation of the first resonance while no longer compensating the first anti&amp;ndash;resonance. 

Improved Curve–Flattening for Flexible Space Robotics

In a multi-agent system, the complex interaction among agents is one of the difficulties in making the optimal decision. This paper proposes a new action value function and a learning mechanism based on the optimal equivalent action of the neighborhood (OEAN) of a multi-agent system, in order to obtain the optimal decision from the agents. In the new Q-value function, the OEAN is used to depict the equivalent interaction between the current agent and the others. To deal with the non-stationary environment when agents act, the OEAN of the current agent is inferred simultaneously by the maximum a posteriori based on the hidden Markov random field model. The convergence property of the proposed methodology proved that the Q-value function can approach the global Nash equilibrium value using the iteration mechanism. The effectiveness of the method is verified by the case study of the top-coal caving. The experiment results show that the OEAN can reduce the complexity of the agents’ interaction description, meanwhile, the top-coal caving performance can be improved significantly.

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Multi-Agent Reinforcement Learning with Optimal Equivalent Action of Neighborhood

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Multi-Agent Reinforcement Learning with Optimal Equivalent Action of Neighborhood