This thesis investigates the occurrence and mitigation of Sequential Social Dilemmas (SSDs) in Local Energy Communities (LECs) managed through Multi-agent Reinforcement Learning (MARL). LECs have great potential as pivotal elements in the green energy transition, yet the inherent conflict between individual incentives and community-wide objectives creates SSD scenarios that challenge learning processes. To address these issues, we propose an agent-centric approach and develop a custom MARL environment where agents interact via a communal battery system and a local trading mechanism.

We systematically investigate the impact of resource constraints and social interactions on the agents' learning. In non-cooperative settings, limited resources impede policy optimization, while the introduction of a shared battery reveals SSD dynamics driven by both greed and fear factors. Our experiments show that rescaling the training data leads agents to adopt more cooperative behaviors, and that reward function modifications incentivizing community-friendly battery use cause a significant increase in social welfare. These mitigation techniques are further validated in a realistic LEC environment with multiple, heterogeneous households engaging in trading and storage actions.

The contributions of this thesis are threefold: (1) the proposal of a new agent-centric MARL environment for LECs, (2) the demonstration of SSDs impacting MARL performance in these decentralized energy systems, and (3) the introduction of concrete strategies for aligning individual and community incentives.

In recent months, researchers developed several new search procedures to augment the process of program synthesis. While many of them performed better than their predecessors, the proposed solutions are still far from ideal. One possible way of overcoming the shortcomings of single search methods is employing genetic algorithms, which have been proven useful in many tasks of similar scale. This paper aims to answer the question of whether it is possible to utilize that sort of algorithms to find an efficient combination of search procedures in a program synthesis problem. An implementation of a genetic algorithm is proposed with parameters and operators chosen through a literature study and a series of experiments on three different domains. To outline different approaches to program synthesis, two fitness functions are examined. Finally, evolved search procedures are discussed and compared with the already existing solutions. One of them in particular, namely a combination of Brute and A* algorithms, manages to surpass their singular counterparts in certain cases.