From 3c4024eb1edc1179f0b2678f8c70d8380b97a4e0 Mon Sep 17 00:00:00 2001 From: Donato Meoli Date: Fri, 26 Jun 2026 03:44:27 +0200 Subject: [PATCH] Fix broken image paths in notebooks (remove files/ prefix) The files/ prefix (a Jupyter classic artifact) does not resolve on GitHub or modern Jupyter since there is no files/ directory; the images live under images/. Point the tags in mdp.ipynb, probability.ipynb and reinforcement_learning.ipynb at images/... directly, consistent with #1302. --- mdp.ipynb | 2 +- probability.ipynb | 4 ++-- reinforcement_learning.ipynb | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/mdp.ipynb b/mdp.ipynb index b9952f528..b348c761b 100644 --- a/mdp.ipynb +++ b/mdp.ipynb @@ -288,7 +288,7 @@ "metadata": {}, "source": [ "Now let us implement the simple MDP in the image below. States A, B have actions X, Y available in them. Their probabilities are shown just above the arrows. We start with using MDP as base class for our CustomMDP. Obviously we need to make a few changes to suit our case. We make use of a transition matrix as our transitions are not very simple.\n", - "" + "" ] }, { diff --git a/probability.ipynb b/probability.ipynb index fe9643a83..14eda4a20 100644 --- a/probability.ipynb +++ b/probability.ipynb @@ -1288,7 +1288,7 @@ "\n", "The example below where we implement the network shown in **Figure 14.3** of the book will make this more clear.\n", "\n", - "\n", + "\n", "\n", "The alarm node can be made as follows: " ] @@ -3275,7 +3275,7 @@ "source": [ "The function **prior_sample** implements the algorithm described in **Figure 14.13** of the book. Nodes are sampled in the topological order. The old value of the event is passed as evidence for parent values. We will use the Bayesian Network in **Figure 14.12** to try out the **prior_sample**\n", "\n", - "\n", + "\n", "\n", "Traversing the graph in topological order is important.\n", "There are two possible topological orderings for this particular directed acyclic graph.\n", diff --git a/reinforcement_learning.ipynb b/reinforcement_learning.ipynb index ee3b6a5eb..585337ac6 100644 --- a/reinforcement_learning.ipynb +++ b/reinforcement_learning.ipynb @@ -128,7 +128,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `sequential_decision_environment` is a GridMDP object as shown below. The rewards are **+1** and **-1** in the terminal states, and **-0.04** in the rest. Now we define actions and a policy similar to **Fig 21.1** in the book." + "The `sequential_decision_environment` is a GridMDP object as shown below. The rewards are **+1** and **-1** in the terminal states, and **-0.04** in the rest. Now we define actions and a policy similar to **Fig 21.1** in the book." ] }, {