The recent advancements in the field of reinforcement learning (RL) for complex robotic tasks and multi-agent systems have shown a significant shift towards more sophisticated and adaptive approaches. Researchers are increasingly leveraging formal languages like Linear Temporal Logic (LTL) to precisely define tasks, leading to the development of adaptive reward functions that encourage measurable progress rather than relying solely on sparse rewards. This shift aims to enhance convergence rates and improve task success rates in RL applications. Additionally, there is a growing focus on addressing the challenges posed by non-Markovian tasks through parallel and modular frameworks, which decompose complex tasks into manageable sub-tasks and train agents in parallel, thereby improving sample efficiency and performance. In the realm of multi-agent systems, the emphasis is on contract-based design and verification methods that facilitate compositional design and verification under quantitative temporal specifications, enhancing scalability and modularity. Furthermore, novel approaches like Temporal-Agent Reward Redistribution (TAR$^2$) are being introduced to tackle the issue of sparse rewards in multi-agent environments by redistributing rewards both temporally and across agents, ensuring optimal policy preservation and accelerating the learning process. These developments collectively represent a move towards more efficient, scalable, and robust RL methodologies for complex and multi-agent scenarios.