Fix doc build errors (#1072)

* Fix links

* Fix README.md links

* Make tutorial runable

* Drop blank page

Author: jeremiahpslewis

.github/workflows/ci.yml

@@ -234,7 +234,7 @@ jobs:
             Pkg.develop(path="../src/ReinforcementLearningEnvironments")
             Pkg.develop(path="../") # ReinforcementLearning meta-package
             Pkg.develop(path="../src/ReinforcementLearningFarm")
-            include("make.jl")' skiplinks # Temporarily skip broken link checks
+            include("make.jl")' # Temporarily skip broken link checks
           mv build homepage/__site/docs
       - name: Deploy to the main repo
         uses: peaceiris/actions-gh-pages@v3

README.md

@@ -54,13 +54,13 @@ The above simple example demonstrates four core components in a general
 reinforcement learning experiment:
 
 - **Policy**. The
-  [`RandomPolicy`](https://juliareinforcementlearning.github.io/docs/rlcore/#ReinforcementLearningCore.RandomPolicy)
+  [`RandomPolicy`](https://juliareinforcementlearning.org/docs/rlcore/#ReinforcementLearningCore.RandomPolicy)
   is the simplest instance of
-  [`AbstractPolicy`](https://juliareinforcementlearning.github.io/docs/rlbase/#ReinforcementLearningBase.AbstractPolicy).
+  [`AbstractPolicy`](https://juliareinforcementlearning.org/docs/rlbase/#ReinforcementLearningBase.AbstractPolicy).
   It generates a random action at each step.
 
 - **Environment**. The
-  [`CartPoleEnv`](https://juliareinforcementlearning.org/docs/rlenvs/#ReinforcementLearningEnvironments.CartPoleEnv-Tuple{})
+  [`CartPoleEnv`](https://juliareinforcementlearning.org/docs/rlenvs/#ReinforcementLearningEnvironments.CartPoleEnv-Tuple%7B%7D)
   is a typical
   [`AbstractEnv`](https://juliareinforcementlearning.org/docs/rlbase/#ReinforcementLearningBase.AbstractEnv)
   to test reinforcement learning algorithms.
@@ -82,17 +82,7 @@ write [blog](https://juliareinforcementlearning.org/blog/) occasionally to
 explain the implementation details of some algorithms. Among them, the most
 recommended one is [*An Introduction to
 ReinforcementLearning.jl*](https://juliareinforcementlearning.org/blog/an_introduction_to_reinforcement_learning_jl_design_implementations_thoughts/),
-which explains the design idea of this package. Besides, a collection of
-[experiments](https://juliareinforcementlearning.org/docs/experiments/) are also provided to help you understand how to train
-or evaluate policies, tune parameters, log intermediate data, load or save
-parameters, plot results and record videos. For example:
-
-<!-- ```@raw html -->
-<img
-src="https://github.com/JuliaReinforcementLearning/ReinforcementLearning.jl/raw/main/docs/src/assets/JuliaRL_BasicDQN_CartPole.gif?sanitize=true"
-width="600px">
-
-<!--
+which explains the design idea of this package.
 
 ## 🙋 Why ReinforcementLearning.jl?
 

docs/make.jl

@@ -37,7 +37,6 @@ makedocs(
             "How to write a customized environment?" => "How_to_write_a_customized_environment.md",
             "How to implement a new algorithm?" => "How_to_implement_a_new_algorithm.md",
             "How to use hooks?" => "How_to_use_hooks.md",
-            "Which algorithm should I use?" => "Which_algorithm_should_I_use.md",
             "Episodic vs. Non-episodic environments" => "non_episodic.md",
         ],
         "FAQ" => "FAQ.md",

docs/src/Which_algorithm_should_I_use.md

@@ -58,6 +58,8 @@ estimate the estimated value of each state-action pair and an explorer to select
 which action to take based on the result of the state-action values.
 
 ```@repl randomwalk1d
+NS = length(S)
+NA = length(A)
 policy = QBasedPolicy(
     learner = TDLearner(
             TabularQApproximator(