Explore the innovative RxEnvironments.jl package in this JuliaCon 2024 talk by Wouter Nuijten. Dive into a reactive programming approach for modeling agent-environment interactions in Reinforcement Learning and self-organizing systems. Learn how this package overcomes limitations of traditional implementations by allowing asynchronous communication and natively supporting multi-agent environments. Discover the concept of Markov blankets and how they're utilized to separate internal states from observable ones. Understand the power of reactivity in decomposing transition functions, enabling more complex and realistic simulations. Compare this approach to traditional frameworks like OpenAI Gym and grasp its advantages in flexibility and scope. Gain insights into a paradigm shift that brings agent-environment interactions closer to real-world scenarios, opening up new possibilities for Reinforcement Learning research and applications.