Reinforcement Learning-Driven Dynamic Investment Strategy Optimization Using Cloud-Based Simulation Frameworks

This study presents a new framework of cloud-based multi-agent reinforcement learning an active dynamic portfolio optimization framework, which overcomes the inherent issues of adaptive asset allocation in changing market environment. The proposed architecture works with dedicated agents which are trained through Proximal Policy Optimization used to identify market regimes in real-time on the basis of which agent contributions are weighted by an attention-based meta-controller. The distributed cloud infrastructure provides the ability to perform simultaneously with experience collection and release asynchronous gradient updates and converges 87% faster than single agent baselines. Detailed analysis on empirical S&P 500 and global ETF indexes data over a series of market cycles indicates significant performance benefits: 21.4% annualized returns and Sharpe ratio of 1.57, or 35.3% better than the same idea using state-of-the-art single-agent deep-reinforcement learning algorithms and 118% compared to conventional mean-variance optimization. The model has strong risk control strength that has a maximum drawdown of 11.8% as opposed to the 24.3% in buy-and-hold models but has high returns in both bullish and high volatility bear markets. The important roles of multi-agent specialization, attention mechanisms and cloud-based scalability are authenticated by ablation studies. These results form a huge breakthrough that can be seen in autonomous portfolio management systems that can dynamically adjust to changing financial environments in real-time.