The accelerating global shift toward renewable energy sources is largely attributed to increased investments and the rising demand for electricity, driven by technological progress, population growth, and escalating fuel prices associated with traditional power generation. Despite their benefits, conventional energy systems face challenges such as sensitivity to fluctuations in solar irradiance and temperature, which lead to non-linear electrical behavior and reduced efficiency. In Iraq, for example, the World Bank reports a significant power distribution loss of approximately 51%. To mitigate these inefficiencies, this study introduces a grid-connected photovoltaic (PV) system employing Maximum Power Point Tracking (MPPT) techniques—specifically, the Perturb and Observe (P&O) and Incremental Conductance (I&C) algorithms. These approaches aim to enhance energy extraction from PV arrays under dynamic environmental conditions. System modeling and performance evaluation were conducted using MATLAB/Simulink, focusing on optimizing output and regulating the DC–DC boost converter’s switching frequency. Under varying irradiance (1000–250 W/m²) and temperature (25–50 °C) conditions, I&C algorithm achieved a higher MPPT tracking efficiency of approximately 98.7%, compared to 95.2% for the P&O method. Additionally, I&C demonstrated faster convergence with a response time of 0.15 s and exhibited reduced power ripple (~ 1.2 kW) versus P&O (~ 3.8 kW), confirming its superior dynamic stability and steady-state performance.

Domain randomization is a widely adopted technique in deep reinforcement learning (DRL) to improve agent generalization by exposing policies to diverse environmental conditions. This paper investigates the impact of different reset strategies, normal, non-randomized, and randomized, on agent performance using the Deep Deterministic Policy Gradient (DDPG) and Twin Delayed DDPG (TD3) algorithms within the CarRacing-v2 environment. Two experimental setups were conducted: an extended training regime with DDPG for 1000 steps per episode across 1000 episodes, and a fast execution setup comparing DDPG and TD3 for 30 episodes with 50 steps per episode under constrained computational resources. A step-based reward scaling mechanism was applied under the randomized reset condition to promote broader state exploration. Experimental results show that randomized resets significantly enhance learning efficiency and generalization, with DDPG demonstrating superior performance across all reset strategies. In particular, DDPG combined with randomized resets achieves the highest smoothed rewards (reaching approximately 15), best stability, and fastest convergence. These differences are statistically significant, as confirmed by -tests: DDPG outperforms TD3 under randomized (, ), normal (, ), and non-randomized (, ) reset conditions. The findings underscore the critical role of reset strategy and reward shaping in enhancing the robustness and adaptability of DRL agents in continuous control tasks, particularly in environments where computational efficiency and training stability are crucial.

Energy efficiency stands as an essential factor when implementing deep reinforcement learning (DRL) policies for robotic control systems. Standard algorithms, including Deep Deterministic Policy Gradient (DDPG), primarily optimize task rewards but at the cost of excessively high energy consumption, making them impractical for real-world robotic systems. To address this limitation, we propose Physics-Informed DDPG (PI-DDPG), which integrates physics-based energy penalties to develop energy-efficient yet high-performing control policies. The proposed method introduces adaptive physics-informed constraints through a dynamic weighting factor (), enabling policies that balance reward maximization with energy savings. Our motivation is to overcome the impracticality of reward-only optimization by designing controllers that achieve competitive performance while substantially reducing energy consumption. PI-DDPG was evaluated in nine MuJoCo continuous control environments, where it demonstrated significant improvements in energy efficiency without compromising stability or performance. Experimental results confirm that PI-DDPG substantially reduces energy consumption compared to standard DDPG, while maintaining competitive task performance. For instance, energy costs decreased from 5542.98 to 3119.02 in HalfCheetah-v4 and from 1909.13 to 1586.75 in Ant-v4, with stable performance in Hopper-v4 (205.95 vs. 130.82) and InvertedPendulum-v4 (322.97 vs. 311.29). Although DDPG sometimes yields higher rewards, such as in HalfCheetah-v4 (5695.37 vs. 4894.59), it requires significantly greater energy expenditure. These results highlight PI-DDPG as a promising energy-conscious alternative for robotic control.