The field of algorithmic trading and market making is experiencing significant developments, driven by the application of reinforcement learning and other advanced techniques. Researchers are exploring new methodologies to improve market making strategies, including the use of deep reinforcement learning, multi-objective reinforcement learning, and novel policy weighting algorithms. These approaches aim to address the challenges of inventory risk, competition, and non-stationary market dynamics, and have shown promising results in terms of outperforming traditional and baseline algorithmic strategies. Additionally, there is a growing focus on understanding the impact of liquidity providers on market stability, with new frameworks and metrics being proposed to characterize their influence. The use of machine learning and artificial intelligence is also being investigated for stock trading and portfolio optimization, with promising results in terms of generating significant profits and outperforming benchmark methods. Noteworthy papers in this area include: Market Making Strategies with Reinforcement Learning, which presents a comprehensive research project on applying reinforcement learning to market making, and SILS: Strategic Influence on Liquidity Stability and Whale Detection in Concentrated-Liquidity DEXs, which offers a detailed approach to characterizing liquidity providers and their impact on market stability. Directly Learning Stock Trading Strategies Through Profit Guided Loss Functions is also notable, as it proposes novel loss functions to drive decision-making for a portfolio of stocks and achieves significant profits compared to benchmark methods.