Reinforcement Learning Notebook (#410)

* initial version (wip)

* fix date and minor corrections

* initial feedback improvements

* improve plot and fix title

* hide plot cell

* add ricardo comment text

* hide plot cell v2

* add manual tag notebook

* hide just input

* shorten notebook

* remove redundant credits section

* fix credits section

* add re-execution date

* omg fix date ....facepalm

* improve last section

* improve style

* remove last plots and add better final remarks

* extend bernoulli comment

* final style review

Author: Juan Orduz

examples/case_studies/reinforcement_learning.ipynb

@@ -48,6 +48,23 @@ @misc{carpenter2016hierarchical
   year          = {2016},
   publisher     = {Technical report. Retrieved from https://mc-stan. org/users/docum entat ion~\ldots{}}
 }
+@article{collinswilson2019,
+  title         = {Ten simple rules for the computational modeling of behavioral data},
+  author        = {Wilson, Robert C and Collins, Anne GE},
+  editor        = {Behrens, Timothy E},
+  volume        = 8,
+  year          = 2019,
+  month         = {nov},
+  pub_date      = {2019-11-26},
+  pages         = {e49547},
+  citation      = {eLife 2019;8:e49547},
+  doi           = {10.7554/eLife.49547},
+  abstract      = {Computational modeling of behavior has revolutionized psychology and neuroscience. By fitting models to experimental data we can probe the algorithms underlying behavior, find neural correlates of computational variables and better understand the effects of drugs, illness and interventions. But with great power comes great responsibility. Here, we offer ten simple rules to ensure that computational modeling is used with care and yields meaningful insights. In particular, we present a beginner-friendly, pragmatic and details-oriented introduction on how to relate models to data. What, exactly, can a model tell us about the mind? To answer this, we apply our rules to the simplest modeling techniques most accessible to beginning modelers and illustrate them with examples and code available online. However, most rules apply to more advanced techniques. Our hope is that by following our guidelines, researchers will avoid many pitfalls and unleash the power of computational modeling on their own data.},
+  keywords      = {computational modeling, model fitting, validation, reproducibility},
+  journal       = {eLife},
+  issn          = {2050-084X},
+  publisher     = {eLife Sciences Publications, Ltd}
+}
 @article{efron1975data,
   title         = {Data analysis using Stein's estimator and its generalizations},
   author        = {Efron, Bradley and Morris, Carl},