## 5 Conclusions and Future Directions  
The energy sector is undergoing a significant transformation, characterized by an increasing focus on decentralization and the adoption of RES. LEMs are emerging as a promising solution to optimize energy distribution within small-scale communities. The energy trading environment in LEMs is susceptible to uncertainties stemming from renewable energy generation and is highly complex due to market dynamics, energy trade price diversity, consumer behavior, interaction, and trading cooperation mechanisms between market participants. In this review paper, initially, different LEM market platforms and projects were investigated and classified into real-time technology-based aggregator platform projects and local market flexibility platform projects. Real-time aggregator platform projects aimed to leverage BCT capabilities and smart contracts to enable transparent tracking of energy transactions, real-time energy trading, data management, and analytics to foster renewable energy adoption at the community level. Local market flexibility platform projects targeted the DERs and demand-side flexibility into the LEM, market facilitation, improved grid management, ancillary services, load balancing, and network planning and development. Our exploration of RL, as a subset of ML, within the context of LEMs has also led to several key findings through an analysis of the literature and various research studies. First and foremost, the dynamic nature of LEMs necessitates flexible decision-making. RL algorithms have demonstrated their capacity to adapt actions effectively in response to changing market dynamics, enabling game players/agents to learn and select optimal actions. We have surveyed various aspects of RL in energy trading, encompassing predictive and analytical approaches within LEMs to enhance decision-making and optimize energy trading strategies. RL methods have shown promise in handling uncertainties by learning from the environment, enabling market players and agents to make informed decisions through the exploration of different strategies and ultimately enhancing rewards. In conclusion, this comprehensive review underscores the considerable potential of RL in optimizing energy trading within LEMs, thereby improving overall market efficiency. The RL techniques also showcased adaptability, robustness, and a capacity to thrive in complex and uncertain environments. Our research has further highlighted that the implementation of LEMs is accompanied by a host of challenges. These challenges include but are not limited to price volatility, demand fluctuations, efficient resource allocation, effective auction and pricing mechanisms, participant interaction and preferences, flexibility, selection of appropriate learning algorithms, real-world implementation, data efficiency, data quality, and scalability. Addressing these challenges is imperative for the successful and widespread adoption of LEMs and necessitates future research efforts and collaborations between academia, industry stakeholders, and policymakers. In the future, the effectiveness of different market structures and regulatory frameworks for LEMs needs to be investigated further to rationalize their role in competitive markets, increase market participation, and ensure fair competition. Researchers may also investigate the potential integration of smart contracts to automate energy trading processes. In addition, more transparent, secure, and verifiable energy transactions for LEMs will require more involvement with BCT and distributed ledger technologies. Privacy and security challenges associated with the increasing amount of data generated in LEMs are another area that needs to be focused on. For seamless communication and collaboration between different market participants and technologies, standardization and interoperability are the obligatory requirements. [PERSON] acknowledges the financial support provided by Higher Education Commission of Pakistan under Ph.D. overseas scholarship Batch 3 Phase 3.