stub.py

import numpy.random as npr
import sys

from SwingyMonkey import SwingyMonkey

class Learner:

  def __init__(self):
    self.last_state  = None
    self.last_action = None
    self.last_reward = None

  def reset(self):
    self.last_state  = None
    self.last_action = None
    self.last_reward = None

  def action_callback(self, state):
    '''Implement this function to learn things and take actions.
    Return 0 if you don't want to jump and 1 if you do.'''

    # You might do some learning here based on the current state and the last state.

    # You'll need to take an action, too, and return it.
    # Return 0 to swing and 1 to jump.

    new_action = npr.rand() < 0.1
    new_state  = state

    self.last_action = new_action
    self.last_state  = new_state

    return self.last_action

  def reward_callback(self, reward):
    '''This gets called so you can see what reward you get.'''

    self.last_reward = reward

iters = 100
learner = Learner()

for ii in range(iters):

  # Make a new monkey object.
  swing = SwingyMonkey(sound=False,            # Don't play sounds.
                       text="Epoch %d" % (ii), # Display the epoch on screen.
                       tick_length=1,          # Make game ticks super fast.
                       action_callback=learner.action_callback,
                       reward_callback=learner.reward_callback)

  # Loop until you hit something.
  while swing.game_loop():
    pass

  # Reset the state of the learner.
  learner.reset()