Update tutorial URL in Readme and documentation

Closes #72

README.md

@@ -2,7 +2,7 @@
 
 # Evoke: A Python package for evolutionary signalling games
 
-+ [Tutorial](https://colab.research.google.com/drive/1AwUCP05lpITAP7_EZD7loGv3unhnwvhM#forceEdit=true&sandboxMode=true) | *Google Colab*
++ [Tutorial](https://colab.research.google.com/drive/1zFRb20KoQi0Tdg5UR1_zReHCZicnQLZd#forceEdit=true&sandboxMode=true) | *Google Colab*
 + [Documentation](https://evoke.readthedocs.io/en/latest/) | *ReadTheDocs*
 + [Package](https://pypi.org/project/evoke-signals/) | *PyPI*
 + [Source code](https://github.com/signalling-games-org/evoke) | *GitHub*
@@ -14,7 +14,7 @@ Evoke comprises a library of methods from **evolutionary game theory** `evoke/sr
 
 ## Online tutorial
 
-See the interactive tutorial on [Google Colab](https://colab.research.google.com/drive/1AwUCP05lpITAP7_EZD7loGv3unhnwvhM#forceEdit=true&sandboxMode=true).
+See the interactive tutorial on [Google Colab](https://colab.research.google.com/drive/1zFRb20KoQi0Tdg5UR1_zReHCZicnQLZd#forceEdit=true&sandboxMode=true).
 
 ## Requirements
 
@@ -30,7 +30,7 @@ Install with pip: `pip install evoke_signals`.
 ### Creating your own signalling games
 
 Creating and running your own simulations requires you to specify payoff matrices, player strategies, and learning or evolutionary dynamics.
-See the [tutorial](https://colab.research.google.com/drive/1AwUCP05lpITAP7_EZD7loGv3unhnwvhM#forceEdit=true&sandboxMode=true) or the [documentation](https://evoke.readthedocs.io/en/latest/) for details and examples.
+See the [tutorial](https://colab.research.google.com/drive/1zFRb20KoQi0Tdg5UR1_zReHCZicnQLZd#forceEdit=true&sandboxMode=true) or the [documentation](https://evoke.readthedocs.io/en/latest/) for details and examples.
 
 ### Recreating figures from the literature
 

docs/source/index.rst

@@ -9,7 +9,7 @@ Evoke: A Python package for evolutionary signalling games
 **Evoke** is a Python library for evolutionary simulations of signalling games.
 It offers a simple and intuitive API that can be used to analyze arbitrary game-theoretic models, and to easily reproduce and customize well-known results and figures from the literature.
 
-* `Tutorial <https://colab.research.google.com/drive/1AwUCP05lpITAP7_EZD7loGv3unhnwvhM#forceEdit=true&sandboxMode=true>`_ | *Google Colab*
+* `Tutorial <https://colab.research.google.com/drive/1zFRb20KoQi0Tdg5UR1_zReHCZicnQLZd#forceEdit=true&sandboxMode=true>`_ | *Google Colab*
 * `Documentation <https://evoke.readthedocs.io/en/latest/>`_ **You are here!** | *ReadTheDocs*
 * `Package <https://pypi.org/project/evoke-signals/>`_ | *PyPI*
 * `Source code <https://github.com/signalling-games-org/evoke>`_ | *GitHub*

docs/source/usage.rst

@@ -20,7 +20,7 @@ To use Evoke, first install it using pip:
 Tutorial
 --------
 
-The best way to discover Evoke is `the interactive tutorial <https://colab.research.google.com/drive/1AwUCP05lpITAP7_EZD7loGv3unhnwvhM#forceEdit=true&sandboxMode=true>`_.
+The best way to discover Evoke is `the interactive tutorial <https://colab.research.google.com/drive/1zFRb20KoQi0Tdg5UR1_zReHCZicnQLZd#forceEdit=true&sandboxMode=true>`_.
 The rest of this document describes very simple use cases.
 
 Creating signalling games
@@ -34,7 +34,7 @@ Creating signalling game simulations requires two kinds of object:
 Games and evolve objects can be mixed and matched.
 This allows you to see differences between evolution and reinforcement learning, by taking the same game and plugging it into different evolve objects.
 
-Take the simple example of a cooperative game with three states, three signals and three acts (the following code examples can also be found in the `tutorial <https://colab.research.google.com/drive/1AwUCP05lpITAP7_EZD7loGv3unhnwvhM#forceEdit=true&sandboxMode=true&scrollTo=304dd76f>`_).
+Take the simple example of a cooperative game with three states, three signals and three acts (the following code examples can also be found in the `tutorial <https://colab.research.google.com/drive/1zFRb20KoQi0Tdg5UR1_zReHCZicnQLZd#forceEdit=true&sandboxMode=true>`_).
 First, in order to create the **game** object, we need to supply Evoke with four pieces of information:
 
 - `state_chances`: The number of states the sender can observe, and their probabilities. For this example there will be three equiprobable states.