Move from rst to md README file.

Author: drvinceknight

README.md

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+This repository hold results for tournaments made possible by the
+[Axelrod-Project](https://github.com/Axelrod-Python/Axelrod) python
+library.
+
+# Tournament result
+
+## Standard tournament
+
+### Ranked violin plot
+
+The mean utility of each player.
+
+![](./assets/strategies_std_boxplot.svg)
+
+### Payoffs
+
+The pair wise utilities of each player.
+
+![](./assets/strategies_std_payoff.svg)
+
+### Evolutionary dynamics
+
+The evolutionary dynamic of the strategies (based on the utilities).
+
+![](./assets/strategies_std_reproduce.svg)
+
+### Wins
+
+The number of wins of each player.
+
+![](./assets/strategies_std_winplot.svg)
+
+### Payoff differences
+
+The payoff differences for each player.
+
+![](./assets/strategies_std_sdvplot.svg)
+
+### Pairwise payoff differences
+
+The difference of payoffs between pairs of players.
+
+![](./assets/strategies_std_pdplot.svg)
+
+### Payoff Matrix
+
+Here is a
+[file with the payoff matrix](./assets/strategies_std_payoff_matrix.csv).
+
+### Summary
+
+Here is a
+[file with the summary data](./assets/std_summary.csv).
+
+## Noisy tournament
+
+### Ranked violin plot
+
+The mean utility of each player.
+
+![](./assets/strategies_noisy_boxplot.svg)
+
+### Payoffs
+
+The pair wise utilities of each player.
+
+![](./assets/strategies_noisy_payoff.svg)
+
+### Evolutionary dynamics
+
+The evolutionary dynamic of the strategies (based on the utilities).
+
+![](./assets/strategies_noisy_reproduce.svg)
+
+### Wins
+
+The number of wins of each player.
+
+![](./assets/strategies_noisy_winplot.svg)
+
+### Payoff differences
+
+The payoff differences for each player.
+
+![](./assets/strategies_noisy_sdvplot.svg)
+
+### Pairwise payoff differences
+
+The difference of payoffs between pairs of players.
+
+![](./assets/strategies_noisy_pdplot.svg)
+
+### Payoff Matrix
+
+Here is a
+[file with the payoff matrix](./assets/strategies_noisy_payoff_matrix.csv).
+
+### Summary
+
+Here is a
+[file with the summary data](./assets/noisy_summary.csv).
+
+## Probabilistic ending tournament
+
+### Ranked violin plot
+
+The mean utility of each player.
+
+![](./assets/strategies_probend_boxplot.svg)
+
+### Payoffs
+
+The pair wise utilities of each player.
+
+![](./assets/strategies_probend_payoff.svg)
+
+### Evolutionary dynamics
+
+The evolutionary dynamic of the strategies (based on the utilities).
+
+![](./assets/strategies_probend_reproduce.svg)
+
+### Wins
+
+The number of wins of each player.
+
+![](./assets/strategies_probend_winplot.svg)
+
+### Payoff differences
+
+The payoff differences for each player.
+
+![](./assets/strategies_probend_sdvplot.svg)
+
+### Pairwise payoff differences
+
+The difference of payoffs between pairs of players.
+
+![](./assets/strategies_probend_pdplot.svg)
+
+### Payoff Matrix
+
+Here is a
+[file with the payoff matrix](./assets/strategies_probend_payoff_matrix.csv).
+
+### Summary
+
+Here is a
+[file with the summary data](./assets/probend_summary.csv).
+
+# Reproducing these results:
+
+To reproduce these results you will need to install the `axelrod`
+library:
+
+    $ pip install axelrod
+
+To reproduce these results run:
+
+    python run_noisy.py  # Run the noisy tournament
+    python run_std.py  # Run the standard tournament
+    python run_probend.py  # Run the probabilistic ending tournament
+
+You can also run all three tournaments (in series):
+
+    python run_all.py
+
+**Note that this uses the installed version of the axelrod library.** If
+you want to keep things tidy you can create a virtualenv and install the
+latest version of the library like so:
+
+    $ virtualenv env
+    $ source env/bin/activate
+    $ python -m pip install git+https://github.com/Axelrod-Python/Axelrod@master
+
+If you have the Axelrod repository locally you can also run:
+
+    $ python -m pip install path_to_axelrod
+
+If you have already installed `axelrod` you can add the [-U]{.title-ref}
+tag to update to the latest version of master:
+
+    $ python -m pip install git+https://github.com/Axelrod-Python/Axelrod@master -U
+
+or:
+
+    $ python -m pip install path_to_axelrod  -U