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UID:pretalx-juliacon2024-UKB7XN@pretalx.com
DTSTART;TZID=CET:20240712T115000
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DESCRIPTION: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 metaprogra
 mming functionality to transform high-level user code into correct Julia s
 yntax. In the newest release of GraphPPL\, users can use any GraphPPL mode
 l as a submodel in larger models\, introducing modularity into probabilist
 ic programming.
DTSTAMP:20260307T204113Z
LOCATION:For Loop (3.2)
SUMMARY:What's new with GraphPPL.jl? - Dmitry Bagaev\, Wouter Nuijten
URL:https://pretalx.com/juliacon2024/talk/UKB7XN/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-juliacon2024-83ZWZN@pretalx.com
DTSTART;TZID=CET:20240712T160000
DTEND;TZID=CET:20240712T163000
DESCRIPTION:In reinforcement learning problems\, interactions between an ag
 ent and its environment are simulated in discrete time steps: after a pre-
 determined amount of time passes\, the agent and environment exchange obse
 rvations and actions that are used to generate new observations for the ag
 ent\, etc. RxEnvironments.jl changes this assumption and uses a Reactive P
 rogramming framework to model agent-environment interactions\, allowing in
 teractions at any time and natively supporting multi-agent environments
DTSTAMP:20260307T204113Z
LOCATION:REPL (2\, main stage)
SUMMARY:RxEnvironments: Reactive multi-agent environments - Wouter Nuijten
URL:https://pretalx.com/juliacon2024/talk/83ZWZN/
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