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Popular pages
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Reinforcement Learning to Learn MDPs
Reinforcement Learning to Learn MDPs Introduction Previous chapters assumed that the agent already knew the structure of the environment. In MDPs, the agent knows everything about the environment and ...
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Environments with hidden state: POMDPs
Environments with hidden state: POMDPs Introduction: Learning about the world from observation The previous chapters made two strong assumptions that often fail in practice. First, we assumed the envi...
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Sequential decision problems: MDPs
Sequential decision problems: MDPs Introduction The previous chapter introduced agent models for solving simple, one-shot decision problems. The next few sections introduce sequential problems, where...
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Quick-start guide to the webppl-agents library
Quick-start guide to the webppl-agents library Contents This is a quick-start guide to using the webppl-agents library. For a comprehensive explanation of the ideas behind the library (e.g. MDPs, POM...
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Multi-agent models
Modeling Agents with Probabilistic Programs Multi-agent models In this chapter, we will look at models that involve multiple agents reasoning about each other. This chapter is based on reft:stuhlmuell...
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Joint inference of biases and preferences I
Joint inference of biases and preferences I Introduction Techniques for inferring human preferences and beliefs from their behavior on a task usually assume that humans solve the task (softmax) optima...