Update a2c-full.md

Author: njustesen

docs/a2c-full.md

@@ -94,7 +94,7 @@ At the moment, the policy is just a neural network with some code around it so t
 Additionally, the bot was only trained to play as the home team and would not know how to play on the other side of the field. Let's fix these things so we can watch 
 our agent play, and even play against it. The code that will be presented can also be used to submit your own neural network based bot to the Bot Bowl competition.
 
-The [examples/a2c/a2c_example.py](https://github.com/njustesen/ffai/blob/master/examples/a2c/a2c_agent.py) script implements the ```Agent``` class just like the 
+The [examples/a2c/a2c_agent.py](https://github.com/njustesen/ffai/blob/master/examples/a2c/a2c_agent.py) script implements the ```Agent``` class just like the 
 scripted bots in our previous tutorials. In the constructor of our Agent class, we load in our neural network policy.
 
 ```python
@@ -239,4 +239,4 @@ sometimes score from this situation.
 - Perform reasonable blocks at the line of scrimmage.
 - It's hard to see, but it seems that agent is fouling a lot on the line of scrimmage as well.
 
-Can you come up with better ways to guide the RL agent during training? Can you achieve a higher win rate with more parameters and more training?
+Can you come up with better ways to guide the RL agent during training? Can you achieve a higher win rate with more parameters and more training?