Constrained MDPs and the reward hypothesis

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It's been a looong ago that I posted on this blog. But this should not mean the blog is dead. Slow and steady wins the race, right? Anyhow, I am back and today I want to write about constrained Markovian Decision Process (CMDPs). The post is prompted by a recent visit of Eugene Feinberg , a pioneer of CMDPs, of our department, and also by a growing interest in CMPDs in the RL community (see this , this , or this paper). For impatient readers, a CMDP is like an MDP except that there are multiple reward functions, one of which is used to set the optimization objective, while the others are used to restrict what policies can do. Now, it seems to me that more often than not the problems we want to solve are easiest to specify using multiple objectives (in fact, this is a borderline tautology!). An example, which given our current sad situation is hard to escape, is deciding what interventions a government should apply to limit the spread of a virus while maintaining economic
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Optogenetics

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This is a little deviation from the usual topic.
Scientist are able to genetically modify neurons that respond to light. They are in fact able to do this in a targeted manner. A patient would then have some LEDs inside his skull, emitting some light. In response the selected neurons start to fire. They demonstrated the technology by making mice run counterclockwise when they turn on the light. This is input to the brain. Earlier, it was demonstrated that neurons can be genetically modified to emit light when they are firing. Are we heading towards rewiring the brain and turning it into a light computer?
The motivation for the research is to cure diseases like Parkinson's disease, when the patient has all the circuity and muscles but is just unable to make the movements. In fact, the researchers are already testing this technology on primates. Source: Wired Nov. 2009, "Powered by Photons" pp. 109--113. The wikipedia entry for optogenetics is here.

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Constrained MDPs and the reward hypothesis

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It's been a looong ago that I posted on this blog. But this should not mean the blog is dead. Slow and steady wins the race, right? Anyhow, I am back and today I want to write about constrained Markovian Decision Process (CMDPs). The post is prompted by a recent visit of Eugene Feinberg , a pioneer of CMDPs, of our department, and also by a growing interest in CMPDs in the RL community (see this , this , or this paper). For impatient readers, a CMDP is like an MDP except that there are multiple reward functions, one of which is used to set the optimization objective, while the others are used to restrict what policies can do. Now, it seems to me that more often than not the problems we want to solve are easiest to specify using multiple objectives (in fact, this is a borderline tautology!). An example, which given our current sad situation is hard to escape, is deciding what interventions a government should apply to limit the spread of a virus while maintaining economic
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