Readme update to desired interface

README.md

 # Dec-MCTS
 
 Python implementation of Dec-MCTS
+
+### Installation
+```bash
+pip install git+https://code.research.uts.edu.au/bigprint/pydecmcts.git
+```
+
+### Usage
+
+```python
+from DecMCTS import Tree
+
+# data can be anything required to calculate your
+# global reward and available actions
+data = {}
+
+# Create an available actions function
+# This returns a list of possible actions to take from a given state
+# state input explained next
+def avail_actions(data, state):
+    
+    # This example is simply getting max sum, 
+    # options are same regardless of state
+    return [1,2,3,4,5]
+
+# Create a reward function. This is the global reward given a list of
+# actions taken by the current robot, and every other robot
+# State is a dictionary with keys being robot IDs, and values
+# are a list of actions taken from the starting position
+def reward(dat, state):
+    each_robot_sum = [sum(state[1][a]) for a in state[1]]
+    return sum(each_robot_sum)
+
+# Number of Action Sequences to communicate
+comm_n = 5
+
+# Create instances for each robot
+tree1 = Tree(data, reward, avail_actions, comm_n, 1) # Robot ID is 1
+tree2 = Tree(data, reward, avail_actions, comm_n, 2) # Robot ID is 2
+
+for i in range(350):
+    tree1.grow()
+    tree2.grow()
+    tree1.receive_comms(tree2.send_comms(), 2) #send comms message doesn't have ID in it
+    tree2.receive_comms(tree2.send_comms(), 1) 
+```