Update Readme

Author: marcharper

README.md

@@ -8,6 +8,26 @@ pip install axelrod numpy cloudpickle
 
 Clone this repository
 
+## Some Changes
+
+In the original repository the strategies were run against all the default strategies in the Axelrod library. This is slow and probably not necessary. For example the Meta* players are just combinations of the other players, and very computationally intensive; it's probably ok to remove those.
+
+This fork uses a subset of about 90 strategies, excluding the most computationally intensives (e.g. the hunters).
+
+## The strategies
+
+The LookerUp strategies are based on lookup tables with two parameters:
+* n, the number of rounds of trailing history to use and
+* m, the number of rounds of initial opponent play to use
+
+### Open questions
+
+* What's the best table for n, m?
+* What's the best table against parameterized strategies. For example, if the opponents are `[RandomPlayer(x) for x in np.arange(0, 1, 0.01)], what lookup table is best? Is it much different from the generic table?
+* Can we separate n into n1 and n2 where different amounts of history are used for the player and the opponent?
+* Incorporate @GKDO's swarm model that makes the tables non-deterministic, for the same values of n and m. Does this produce better results for all n and m?
+
+
 ## Running
 
 `python lookup-evolve.py -h`
@@ -16,7 +36,7 @@ will display help. There are a number of options and you'll want to set the muta
 
 
 Here are some recommended defaults:
-
+```
 python lookup_evolve.py -p 3 -s 3  -g 100000 -k 20 -u 0.01 -b 20 -i 4 -o evolve3-3.csv
 
 python lookup_evolve.py -p 3 -s 2  -g 100000 -k 20 -u 0.03 -b 20 -i 4 -o evolve3-2.csv
@@ -26,11 +46,15 @@ python lookup_evolve.py -p 3 -s 1  -g 100000 -k 20 -u 0.06 -b 20 -i 4 -o evolve3
 python lookup_evolve.py -p 1 -s 3  -g 100000 -k 20 -u 0.03 -b 20 -i 4 -o evolve1-3.csv
 
 python lookup_evolve.py -p 2 -s 3  -g 100000 -k 20 -u 0.03 -b 20 -i 4 -o evolve2-3.csv
-
+```
 ### 2, 2 is the current winner:
-
+```
 python lookup_evolve.py -p 2 -s 2  -g 100000 -k 20 -u 0.06 -b 20 -i 4 -o evolve2-2.csv
-
+```
 ### 4, 4
+```
+python lookup_evolve.py -p 4 -s 4  -g 100000 -k 20 -u 0.01 -b 20 -i 4 -o evolve4-4.csv
+```
+## Analyzing
 
-python lookup_evolve.py -p 4 -s 4  -g 100000 -k 20 -u 0.01 -b 20 -i 4 -o evolve4-4.csv
+The output files `evolve{n}-{m}.csv` can be easily sorted by `analyze_data.py`, which will output the best performing tables. These can be added back into Axelrod.