Update example imports

README.md

@@ -5,9 +5,9 @@
 Welcome to gameai! This package contains a series of well-defined abstractions that are common in AI, such as a games, agents, and trainers which optimize the behavior of agents. As long as a class inherits from the base implemetation of a primitive (e.g. `Agent`) and implements the required methods, it can be used in place of the standard implementations given. To get started:
 
 ```python
-from games import TicTacToe
-from agents import RandomAgent, MCTSAgent
-from core import Arena, Player
+from gameai.games import TicTacToe
+from gameai.agents import RandomAgent, MCTSAgent
+from gameai.core import Arena, Player
 
 # Create our game
 game = TicTacToe()

examples/random_vs_mcts.py

@@ -1,6 +1,6 @@
-from games import TicTacToe
-from agents import RandomAgent, MCTSAgent
-from core import Arena, Player
+from gameai.games import TicTacToe
+from gameai.agents import RandomAgent, MCTSAgent
+from gameai.core import Arena, Player
 
 
 def play():

gameai/agents/trainable_agent.py

@@ -12,7 +12,7 @@ class TrainableAgent(Agent):
 
     def train(self, g, **kwargs):
         '''
-        Train the agent. As a convenience this should return self.training_params()
+        Train the agent. As a convenience this should return :code:`self.training_params()`
         at the end of training
         '''
         raise NotImplementedError

sphynx/getting-started.rst

@@ -16,9 +16,9 @@ Basic Example
 
 .. code-block:: python
 
-  from games import TicTacToe
-  from agents import RandomAgent, MCTSAgent
-  from core import Arena, Player
+  from gameai.games import TicTacToe
+  from gameai.agents import RandomAgent, MCTSAgent
+  from gameai.core import Arena, Player
 
   # Create our game
   game = TicTacToe()