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X-WR-CALNAME:When Should Reinforcement Learning Use Causal Reasoning? - Oli
 ver Schulte\, Professor\, Simon Fraser University
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TZUNTIL:20261101T090000Z
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DTSTART:20241103T020000
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DESCRIPTION:Zoom Link Abstract: Reinforcement learning (RL) and causal mode
 lling naturally complement each other. The goal of causal modelling is to 
 predict the effects of interventions in an environment\, while the goal of
  reinforcement learning is to select interventions that maximize the rewar
 ds the agent receives from the environment. Reinforcement learning include
 s the two most powerful sources of information for estimating causal relat
 ionships: temporal ordering and the ability to act on an environment. This
  paper examines which reinforcement learning settings we can expect to ben
 efit from causal…
DTSTART;TZID=America/Vancouver:20241118T130000
DTEND;TZID=America/Vancouver:20241118T140000
LAST-MODIFIED:20241031T195232Z
LOCATION:UBC Vancouver Campus\, ICCS X836 / Zoom
SUMMARY:When Should Reinforcement Learning Use Causal Reasoning? - Oliver S
 chulte\, Professor\, Simon Fraser University
TRANSP:OPAQUE
URL:https://caida.ubc.ca/event/when-should-reinforcement-learning-use-causa
 l-reasoning-oliver-schulte-professor-simon-fraser
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