Comparative Analysis of Federated Learning (FL), Reinforcement Learning (RL) and Evolution Strategy (ES) in Gaming Environments

Sequential decision-making (SDM) is the process of choosing the best actions in a Gaming Environment, step by step over time. But it has many challenges, including that rewards are rare or misleading and Environments are different for each agent. Reinforcement Learning (RL) and Evolution Strategies (ES) are two popular ways to solve SDM problems. Since RL uses gradients and value-based learning and ES works like a black-box and uses a group of solutions (population-based), this paper compares RL and ES in both centralized and federated learning situations. It also uses simple games like Snake Game, Mountain Car, CartPole, and Assault (an Atari game) for testing. The study compares (1) how fast RL and ES learn (convergence) and how much communication they need. This study helps us choose the right method for real-life cases with limited resources, privacy concerns, and decentralized learning environments.