Reinforcement Learning

Human Choice and Reinforcement Learning (3)

1. Goals of Paramter Estimation When estimating paramters for a given model, we typically aim to make an inference on an individual’s underlying decision process. We may be inferring a variety of different factors, such as the rate at which someone updates their expectations, the way that someone subjectively values an outcome, or the amount of exploration versus exploitation that someone engages in.

Sep 8, 2018 16 min read Reinforcement Learning

Human Choice and Reinforcement Learning (2)

1. Answer to post 1 In the previous post, I reviewed the Rescorla-Wagner updating (Delta) rule and its contemporary instantiation. At the end, I asked the following question:

Apr 7, 2017 9 min read Reinforcement Learning

Human Choice and Reinforcement Learning (1)

1. Short history In 1972, Robert Rescorla and Allan Wagner developed a formal theory of associative learning, the process through which multiple stimuli are associated with one-another. The most widely used example (Fig.

Apr 4, 2017 8 min read Reinforcement Learning