Exploration Strategy Research Articles

Curious Explorer: a Provable Exploration Strategy in Policy Learning.

A coverage assumption is critical with policy gradient methods, because while the objective function is insensitive to updates in unlikely states, the agent may need improvements in those states to reach a nearly optimal payoff. However, this assumption can be unfeasible in certain environments, for instance in online learning, or when restarts are possible only from a fixed initial state. In these cases, classical policy gradient algorithms like REINFORCE can have poor convergence properties and sample efficiency. Curious Explorer is an iterative state space pure exploration strategy improving coverage of any restart distribution ρ. Using ρ and intrinsic rewards, Curious Explorer produces a sequence of policies, each one more exploratory than the previous one, and outputs a restart distribution with coverage based on the state visitation distribution of the exploratory policies. This paper main results are a theoretical upper bound on how often an optimal policy visits poorly visited states, and a bound on the error of the return obtained by REINFORCE without any coverage assumption. Finally, we conduct ablation studies with REINFORCE and TRPO in two hard-exploration tasks, to support the claim that Curious Explorer can improve the performance of very different policy gradient algorithms.

Integrating the Great Anti-Epidemic Spirit Prevention into Patriotic Education for Medical Students: Theoretical Exploration and Practical Strategies for Value Symbiosis

抗疫斗争中形成了伟大的抗疫精神蕴含着丰富的精神内涵和重要的思想政治教育价值，是对医学生进行爱国主义教育的优质资源。将抗疫精神融入医学生爱国主义教育，能够培养医学生的敬畏生命、家国情怀、无私奉献和求真务实与守望相助的优良品格。通过优化思政课堂内容、深度融合抗疫精神资源、构建人文教育生态特色环境以及利用网络思政教育新渠道构建医学生爱国主义教育路径，能有效提升医学生思想政治教育的实效性，使其在未来职业生涯中更好地服务于国家和社会。

Shortwave infrared (SWIR) spectroscopy for greenfield exploration: Investigating the Bayi-Muchang prospect within the Jiama giant Porphyry-Skarn system

Jiama is a giant porphyry-skarn system (1,814 Mt ore @ 0.40 % Cu) within the Gangdese porphyry copper belt, Southern Tibet. Recently, copper, molybdenum, and tungsten anomalies, similar to those at Jiama, have been identified in the Bayi-Muchang prospect southwest of the deposit. However, the mineralization potential of this peripheral area and its genetic connection to Jiama remain poorly constrained. This study employs shortwave infrared (SWIR) spectroscopy to map alteration minerals in both the Bayi-Muchang prospect and compare them to those in the southwestern Jiama deposit. SWIR spectral results reveal that the Bayi-Muchang prospect is dominated by chlorite, phengite, quartz, and less carbonates from chlorite-sericite alteration with minor epidote and chlorite vein-halos, as well as pervasive propylitic assemblages with chlorite, epidote, and less carbonates, indicating the presence of an independent hydrothermal center where ore-forming fluids were concentrated and exsolved rather than an extension of the Jiama deposit. Key spectral indicators for mineralized zones, derived from Jiama samples, including wavelength of 2200 nm feature longer than 2207 nm and the illite spectral maturity higher than 1.4 in white mica, as well as the full width at half maximum (FWHM) of 2335 nm feature longer than 36 nm in carbonate minerals. Additionally, carbonate minerals in the Duodigou Formation north of Bayi-Muchang record a contact metamorphism gradient, with the FWHM of the 2335 nm feature increasing from 36 to 56 nm from south to north. The northwest Bayi-Muchang area, near the hydrothermal center, is identified as a favorable target for skarn-type and porphyry-type mineralization. This study highlights the effectiveness of SWIR spectroscopy mapping in supporting exploration strategies within porphyry-skarn systems, particularly for greenfield targets.

Estimating petrophysical properties using Geostatistical inversion and data-driven extreme gradient boosting: A case study of late Eocene McKee formation, Taranaki Basin, New Zealand

Estimation of petrophysical properties holds significant importance in evaluating the feasibility of hydrocarbon extraction and enhancing production efficiency. Therefore, theintegration of seismic data with well data for reservoir characterization has garnered significant attention in the oil and gas industry. This approach provides a comprehensive view of the lateral variations across the entire reservoir. In this study, our goal is to establish a relationship between seismic attributes and the petrophysical properties of the reservoir, enabling the prediction of property distribution across the entire reservoir. We employed geostatistical inversion to generate detailed P-impedance maps of the reservoir, capturing its spatial heterogeneity. A total of 100 realizations were produced and ranked into P10, P50, and P90 scenarios, representing different probabilistic outcomes. The P90 scenario, considered the most representative of the reservoir's average properties, was selected for interpretation, enabling a comprehensive analysis of reservoir heterogeneities and their distribution. Subsequently, we estimated porosity using various seismic attributes, employing a Data-driven Extreme Gradient Boosting (Xgboost) approach within the reservoir analysis. This method enabled us to estimate a high-resolution acoustic impedance and porosity with minimal root mean square error of 3.8 and r2score of 66 % across the spatial gap which ranges from 8468.84 to 9367.85(g/cc) *(m/s) and 10 to 45 % between wells, revealing the lateral variation of acoustic impedance and porosity within the reservoir interval of interest. The information provided was found to be of significant value in the identification of potential sweet spots and plays a pivotal role in the development of a reservoir management strategy for future hydrocarbon exploration.

GRMD: A Two-Stage Design Space Exploration Strategy for Customized RNN Accelerators

Recurrent neural networks (RNNs) have produced significant results in many fields, such as natural language processing and speech recognition. Owing to their computational complexity and sequence dependencies, RNNs need to be deployed on customized hardware accelerators to satisfy performance and energy-efficiency constraints. However, designing hardware accelerators for RNNs is challenged by the vast design space and the reliance on ineffective optimization. An efficient automated design space exploration (DSE) strategy that can balance conflicting objectives is wanted. To address the low efficiency and insufficient universality of the resource allocation process employed for hardware accelerators, we propose an automated two-stage design space exploration (DSE) strategy for customized RNN accelerators. The strategy combines a genetic algorithm (GA) and a reinforcement learning (RL) algorithm, and it utilizes symmetrical exploration and exploitation to find the optimal solutions. In the first stage, the area of the hardware accelerator is taken as the optimization objective, and the GA is used for partial exploration purposes to narrow the design space while maintaining diversity. Then, the latency and power of the hardware accelerator are taken as the optimization objectives, and the RL algorithm is used in the second stage to find the corresponding Pareto solutions. To verify the effectiveness of the developed strategy, it is compared with other algorithms. We use three different network models as benchmarks: a vanilla RNN, LSTM, and a GRU. The results demonstrate that the strategy proposed in this paper can provide better solutions and can achieve latency, power, and area reductions of 9.35%, 5.34%, and 11.95%, respectively. The HV of GRMD is reduced by averages of 6.33%, 6.32%, and 0.67%, and the runtime is reduced by averages of 18.11%, 14.94%, and 10.28%, respectively. Additionally, given different weights, it can make reasonable trade-offs between multiple objectives.

Physical and experimental simulation of unconventional reservoir formation for carboniferous in hudson oilfield, Tarim Basin, China

This study delves into the formation mechanisms of unconventional oil reservoirs located within the Carboniferous strata of the geologically intricate Hudson Oilfield, situated in the Tarim Basin, integrating extensive geological survey data with a sophisticated, physically simulated cross-sectional model specifically constructed for this study. This integrated approach enables a detailed examination of the distribution of interlayers and their profound effects on reservoir heterogeneity, as well as the non-equilibrium dynamics at the oil-water interface. Key findings reveal that randomly distributed calcareous interlayers significantly increase reservoir compartmentalization, raising heterogeneity indices by 30%, while oil-water interface inclinations exceeding 100 m were observed in 20% of the studied reservoirs, along with lateral hydrocarbon reversals, challenging traditional knowledge. Variations in porosity and permeability have led to a 45% discrepancy in estimations of recoverable reserves, underscoring the complexity of these systems. Advanced simulation techniques have improved the accuracy of predicting unconventional reservoir characteristics by 25% over conventional geological methods, highlighting the importance of incorporating reservoir instability and the complexity of interlayer structures into the analysis of unconventional hydrocarbon systems. These findings significantly advance our understanding of Carboniferous unconventional reservoir evolution, offering new perspectives on the role of these factors and informing more effective exploration strategies and enhanced efficiency in hydrocarbon recovery processes.

A hybrid metaheuristic algorithm for antimicrobial peptide toxicity prediction

The development of new algorithms can aid researchers and professionals in resolving problems that were once unsolvable or discovering superior solutions to problems that were already settled. By recognizing the importance of continuous research on creating novel algorithms, this paper introduced a hybrid metaheuristic algorithm-h-PSOGNDO, which is a combination of Particle Swarm Optimization (PSO) and Generalized Normal Distribution Optimization (GNDO). The proposed algorithm utilizes the Particle Swarm Optimization’s strategy for exploitation and the Generalized Normal Distribution Optimization’s global search strategy for exploration. Through this combination, h-PSOGNDO is believed to be an effective algorithm that can promote the advantages of its parents’ algorithms. Different assessment methods are used to assess the proposed novel algorithm. First, the h-PSOGNDO is set to conduct experiments on two sets of mathematical functions, including twenty-eight IEEE CEC2017 and ten IEEE CEC2019 benchmark test functions, respectively. Then, the h-PSOGNDO algorithm is applied to a case study on the prediction of antimicrobial peptides’ toxicity to evaluate its performance on real-life problems. The statistical findings collected from both the test function sets and the case study show that the h-PSOGNDO algorithm works effectively, proving its astonishing ability to yield highly competitive outcomes for complex problems.

A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search

Monte-Carlo Tree Search (MCTS) is a class of methods for solving complex decisionmaking problems through the synergy of Monte-Carlo planning and Reinforcement Learning (RL). The highly combinatorial nature of the problems commonly addressed by MCTS requires the use of efficient exploration strategies for navigating the planning tree and quickly convergent value backup methods. These crucial problems are particularly evident in recent advances that combine MCTS with deep neural networks for function approximation. In this work, we propose two methods for improving the convergence rate and exploration based on a newly introduced backup operator and entropy regularization. We provide strong theoretical guarantees to bound convergence rate, approximation error, and regret of our methods. Moreover, we introduce a mathematical framework based on the use of the α-divergence for backup and exploration in MCTS. We show that this theoretical formulation unifies different approaches, including our newly introduced ones, under the same mathematical framework, allowing to obtain different methods by simply changing the value of α. In practice, our unified perspective offers a flexible way to balance between exploration and exploitation by tuning the single α parameter according to the problem at hand. We validate our methods through a rigorous empirical study from basic toy problems to the complex Atari games, and including both MDP and POMDP problems.

Address wind farm layout problems by an adaptive Moth-flame Optimization Algorithm

As a clean renewable energy source, wind energy has been widely used to generate electricity in wind farms. Plenty of turbines work together to form a wind farm to increase electricity output. However, this produces an inevitable wake effect, which affects the efficiency of turbines in capturing wind energy, further leading to a decrease in the power generated by wind farms. To minimize the wake effect, optimizing the turbines layout in wind farms is necessary. Therefore, an adaptive Moth-Flame Optimization algorithm with enhanced Exploration and Exploitation capabilities (MFOEE) is proposed. The refinements of the algorithm include: i) defining a selection probability to utilize diverse information of moths; ii) an enhanced exploitation strategy by pushing moths towards the best flame; iii) developing an enhanced exploration strategy, in which three moths exchange information and two inferior moths fly to the superior moth. To validate the performance of MFOEE, four scenarios are set up using grid-based simulations of actual wind farm conditions. The experimental findings demonstrate that MFOEE can offer the most optimal scheme among the four scenarios, which indicates that MFOEE proves highly effective in addressing wind farm layout optimization challenges.

Distribution Characteristics of Trace Elements in Carboniferous–Permian Coal from the Western Margin of Ordos Basin: Emphasis on Their Complex Geological Genesis

The Carboniferous–Permian coal deposits in the western margin of the Ordos Basin are known for their unique geological characteristics and potential enrichment of trace elements; however, there have been limited studies on the complex geological genesis of these elements, hindering the development of effective strategies for mineral resource exploration in this region. This study aims to investigate the distribution characteristics of trace elements in Carboniferous–Permian coal from the western margin of Ordos Basin, focusing on their complex geological genesis using techniques such as optical microscopy, X-ray fluorescence spectrometry, and inductively coupled plasma mass spectrometry. The results show that the average maximum vitrinite reflectance values in the Helanshan coalfield, Zhuozishan coalfield, and Ningdong coalfield are 1.25%, 0.83%, and 0.69%, respectively. Compared with the world’s hard coals, Li and Ga in Carboniferous–Permian coal from the western margin of the Ordos Basin are mildly enriched (CC, concentration coefficients; 2 < CC < 5) or enriched (5 < CC < 10). On the basis of revealing the response of the geochemical characteristics of coal to the geological development of the basin, the composite genetic model of terrigenous clastic supply, fault structure, low-temperature hydrothermal fluid and coal metamorphism have been established in Carboniferous–Permian coal in the western margin of the Ordos Basin. In this complex genetic model, folds and faults are very well developed. Although the provenance may have provided sufficient detrital sources for the study area, frequent tectonic changes, denudation, or scour led to the loss of detrital supply, and the provenance did not ultimately cause the enrichment of elements in the study area. However, the widely developed fault structure provided channels for sulfur-containing hydrothermal fluids, and the increase in coal metamorphism resulted in the enrichment of trace elements in the Carboniferous–Permian coal in the western margin of the Ordos Basin.

Proton exchange membranes from double-filler of sulfonated titanium dioxide nanotubes and graphitic carbon nitride nanosheets integrated into sulfonated poly(aryl ether sulfone)s

Inorganic-organic composite proton exchange membranes (PEMs) are promising substitutes in proton exchange membrane fuel cells. To improve the compatibility between inorganic fillers and polyelectrolytes, along with alleviating filler aggregation, a series of composite PEMs incorporating both sulfonated titanium dioxide nanotubes (sTiNT) and graphitic carbon nitride nanosheets (g-C3N4) into sulfonated poly (aryl ether sulfone) s (SPAES) have been prepared. The distinct morphology of sTiNT and g-C3N4, coupled with their respective sulfonic acids and amine/imine functionalities, endow the fillers with exceptional compatibility and dispersivity within SPAES. The obtained SPAES/sTiNT/g-C3N4 membranes exhibit superior thermal, size and mechanical stability, robust oxidative resistance, together with high proton conductivity. The SPAES/sTiNT/g-C3N4(1/3) membrane particularly exhibits proton conductivity of 202 mS/cm and swelling below 10% (90 °C), a hydrogen fuel cell output of 512 mW/cm2 (80 °C), which is 1.6 times of the SPAES membrane. The results suggest the great potential of multi-filler compositing strategy for alternative PEMs exploration.

Curiosity-Driven Multi-Level Intrinsic Reward DQN for Enhanced Exploration in Reinforcement Learning

Abstract. Reinforcement learning (RL) has shown great potential in solving complex decision-making tasks. However, efficient exploration remains a significant challenge, particularly in environments with sparse or deceptive rewards. This paper introduces DQN-Mult-Cur, a novel reinforcement learning algorithm that enhances exploration by integrating curiosity-driven and multi-level intrinsic rewards within the Deep Q-Network (DQN) framework. The proposed method addresses the limitations of conventional exploration strategies by incentivizing agents to explore novel and meaningful states, thereby improving learning efficiency and performance. Extensive experiments across three standard environmentsCartPole-v1, MountainCar-v0 and Acrobot-v1demonstrate that DQN-Mult-Cur outperforms traditional DQN variants, achieving faster convergence, higher rewards, and greater stability. An ablation study further highlights the importance of each intrinsic reward component, confirming the robustness of the proposed approach. The results suggest that DQN-Mult-Cur offers a comprehensive solution to the exploration-exploitation trade-off in reinforcement learning, making it applicable to a wide range of challenging environments.

An adaptive exploration mechanism for Q-learning in spatial public goods games

The Q-learning algorithm has been widely applied to investigate the emergence of cooperation in social dilemmas. Despite ϵ -greedy being the most common exploration strategy in Q-learning, mechanisms for adjusting exploration as the game environment changes have not been thoroughly researched. To stay close to reality, this paper proposes an environment-adaptive exploration-based Q-Learning algorithm. We applied the registration concept from image processing to characterize agents’ sensitivity to changes in their surrounding environment to obtain local stimulation. Additionally, we calculated the advantage differences between the agent and the global environment to acquire global stimulation. Simulation results on the public goods game show that the level of cooperation increases and the fraction of exploration consequently decreases when the agents focus more on the local environment. However, the impact of the basic exploration rate on the level of cooperation is not uniform: when the enhancement factor is low, an increase in the exploration rate promotes cooperation, while when the enhancement factor is high, increasing the exploration rate reduces the level of cooperation. The basic exploration rate directly affects the fraction of exploration. Therefore, increasing the basic exploration rate can stably increase the fraction of exploration of the agents. Similarly, the effect of the memory strength parameter λ on the level of cooperation is positively correlated, and increasing the value of λ increases the level of cooperation across the board. These evolutionary dynamics could enrich the understanding of cooperation in complex systems.

Robust quantum dots charge autotuning using neural network uncertainty

Abstract This study presents a machine learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This method exploits artificial neural networks to identify noisy transition lines in stability diagrams, guiding a robust exploration strategy leveraging neural network uncertainty estimations. Tested across three distinct offline experimental datasets representing different single-quantum-dot technologies, this approach achieves a tuning success rate of over 99% in optimal cases, where more than 10% of the success is directly attributable to uncertainty exploitation. The challenging constraints of small training sets containing high diagram-to-diagram variability allowed us to evaluate the capabilities and limits of the proposed procedure.

Active learning strategies for the design of sustainable alloys.

Active learning comprises machine learning-based approaches that integrate surrogate model inference, exploitation and exploration strategies with active experimental feedback into a closed-loop framework. This approach aims at describing and predicting specific material properties, without requiring lengthy, expensive or repetitive experiments. Recently, active learning has shown potential as an approach for the design of sustainable materials, such as scrap-compatible alloys, and for enhancing the longevity of metallic materials. However, in-depth investigations into suited best-practice strategies of active learning for sustainable materials science are still scarce. This study aims to present and discuss active learning strategies for developing and improving sustainable alloys, addressing single-objective and multi-objective learning and modelling scenarios. As model cases, we discuss active learning strategies for optimizing Invar and magnetic alloys, representing single-objective scenarios, and more general steel design approaches, exemplifying multi-objective optimization. We discuss the significance of finding the right balance between exploitation and exploration strategies in active learning and suggest strategies to reduce the number of iterations across diverse scenarios. This kind of research aims to find metrics for a more effective application of active learning and is used here to advance the field of sustainable alloy design.This article is part of the discussion meeting issue 'Sustainable metals: science and systems'.

Depositional and diagenetic heterogeneities in sequence stratigraphic framework of a mixed carbonate-siliciclastic reservoir: A case study from Oligocene–Miocene Asmari Formation in the Persian Gulf

Mixed siliciclastic–carbonate reservoirs, such as the Asmari Formation in the Zagros Basin, present significant heterogeneities that influence hydrocarbon exploration and production. This study examines the depositional and diagenetic heterogeneities of the Asmari Formation in the northwest Persian Gulf through a multi-methodological approach, including petrographic analysis, petrophysical measurements, core CT-scanning, X-ray diffraction, and sieve analysis. The primary goal is to identify the origins, scales, and predictability of these heterogeneities within a sequence stratigraphic framework to enhance reservoir characterization. Facies analysis reveals depositional environments ranging from sabkha to shoreface, peritidal, lagoonal, and shoal settings, with carbonate sedimentation interrupted by clastic influxes associated with sea-level fluctuations. Diagenetic processes, including dolomitization, dissolution, cementation, and fracturing, were identified through petrographic analysis, highlighting their effects on reservoir quality. To quantify these heterogeneities and delineate reservoir flow units, methods such as Winland classification, hydraulic flow unit (HFU) analysis, and Lorenz zonation were applied. Results show that depositional heterogeneities, predominantly driven by sea-level changes, range from small to large scales and can be predicted within a sequence stratigraphic context. Diagenetic modifications substantially affect porosity and permeability, with dolomitization improving reservoir potential, while compaction and cementation tend to reduce it. Fracturing, particularly in the lower Asmari carbonates, enhances permeability. The study concludes that accurately recognizing and modeling reservoir heterogeneities in three dimensions can significantly improve exploration and development strategies in the Persian Gulf. A better understanding of the spatial distribution of high-quality reservoir zones and impermeable baffle layers will facilitate more precise well placement, optimize hydrocarbon recovery, and mitigate exploration risks.

The formation and recycling of Neoproterozoic granitoids in the Jiangnan Orogen, South China: Implications for Mesozoic rare metal mineralization

In the era of energy transition on the Earth, rare metal mineralization has attained increased significance for various energy sectors and understanding their formation and evolution in various tectonic settings is of great importance for formulating exploration strategies. The Neoproterozoic Jiangnan Orogen in South China marks the assembly zone of the Yangtze and Cathaysia blocks and carries numerous rare metal deposits. In this contribution, we investigate the Neoproterozoic Jiuling composite batholith to understand the formation of rare metal mineralization. We present new in-situ zircon U–Pb ages and Lu–Hf isotopes, and whole-rock geochemistry and Sm–Nd isotopes of the Banbei biotite granodiorite and the associated rare metal-mineralized Baishawo granite in the western portion of the Jiuling batholith suite. Zircon U–Pb dating indicates that the Banbei biotite granodiorite, and the Baishawo two-mica granite and muscovite granite formed at ca. 820 Ma, 153 Ma, and 142 Ma, respectively. The εHf(t) values of the biotite granodiorite, two-mica granite and muscovite granite are –4.4 to +5.8, –11.8 to –7.0, and –10.5 to –7.3, respectively. The εNd(t) values of the biotite granodiorite, two-mica granite and muscovite granite are –3.88 to –2.73, –10.6 to –10.2, and –9.23 to –9.16, respectively. Zircon Hf isotopes and whole-rock Sm–Nd isotopes suggest that the granite suite was sourced from the Mesoproterozoic crust. Geochemical modeling suggests that the Banbei biotite granodiorite was derived from partial melting of Meso- to Neoproterozoic metasedimentary rocks, whereas the Baishawo two-mica granite and muscovite granite were derived from assimilation–fractional crystallization of the Neoproterozoic Banbei biotite granodiorite and Mesozoic magma with low degrees of differentiation. Combined with previous studies on the granite suites in the Jiangnan Orogen, we suggest that collision between the Yangtze and Cathaysia blocks ceased at least 820 Ma, and that Precambrian rocks might have contributed significantly to the enrichment of rare metal mineralization in Mesozoic granite through reworking.

Reinforcement Learning-Based Bandwidth Decision in Optical Access Networks: A Study of Exploration Strategy and Time With Confidence Guarantee

Reinforcement Learning-Based Bandwidth Decision in Optical Access Networks: A Study of Exploration Strategy and Time With Confidence Guarantee

Interactive 3D structural design in virtual reality using preference-based topology optimization

Innovative load-bearing structures often emerge from a fine balance between creative forms and engineering principles. While preference-based topology optimization methods have advanced structural design by considering designers’ geometric preferences, they struggle with visualizing and editing complex 3D details crucial for diverse design options. To overcome this bottleneck, here we propose the improved bidirectional evolutionary structural optimization considering subjective preferences (ISP-BESO) method. This method introduces a similarity constraint that enables precise control over subjective preferences in optimized structures. Then, a design exploration strategy is proposed by integrating virtual reality (VR) with topology optimization for the interactive creation of desirable 3D structures. The strategy employs VR sculpting to offer immersive visualization and real-time feedback, guiding material redistribution during optimization. This workflow can iteratively produce innovative and efficient structures. Adjusting target similarity in ISP-BESO steers designs toward performance-driven or preference-driven outcomes. A museum design example demonstrates the practical potential of this strategy.

Survey Paper on Quantum Deep Reinforcement Learning for Robot Navigation Tasks

Abstract: Quantum deep reinforcement learning is an integration of the principles of quantum computing with deep reinforcement learning in an effort to advance robot navigation tasks. Due to the explosion of the state and action spaces, traditionally DRL methods are limited in scalability and efficiency. QDRL, however, uses quantum superposition and entanglement for more efficient processing of big amounts of information-it enables robots to learn optimal policies for navigating complex environments. We introduce here a new QDRL framework in which quantum neural networks encode policy functions and value estimates. We demonstrate how quantum circuits can take advantage of multi-dimensional state representations in order to strengthen the power of a robot in changing its environment over time. Benchmark experiments on navigation tasks reflect phenomenal acceleration in learning performance along with better decision-making accuracy compared to classical DRL methods. We then consider integrating such quantum algorithms, as Grover's search, to improve exploration strategies in sparse reward settings. The experiments show that QDRL speeds up convergence rates and provides robots with the possibility to react more rapidly to unexpected obstacles. It discusses new applications for quantum computing of autonomous systems, potentially applied to robotics, autonomous vehicles, and other environments which require complex decision making. Some lines of work in the future are optimization of the resources needed for quantum computers and hybrid architectures for the resolution of classical problems in navigation