Wouter Nuijten
PhD student @ Eindhoven University of Technology
Sessions
Probabilistic Programming Languages (PPLs) aim to shield users from the complex mechanics of Bayesian inference. GraphPPL has previously been introduced as the PPL of RxInfer.jl. GraphPPL uses Julia's metaprogramming functionality to transform high-level user code into correct Julia syntax. In the newest release of GraphPPL, users can use any GraphPPL model as a submodel in larger models, introducing modularity into probabilistic programming.
In reinforcement learning problems, interactions between an agent and its environment are simulated in discrete time steps: after a pre-determined amount of time passes, the agent and environment exchange observations and actions that are used to generate new observations for the agent, etc. RxEnvironments.jl changes this assumption and uses a Reactive Programming framework to model agent-environment interactions, allowing interactions at any time and natively supporting multi-agent environments