gistfile1.txt

import gym
import pandas as pd
import numpy as np
import random

# https://gym.openai.com/envs/CartPole-v0
# Carlos Aguayo - carlos.aguayo@gmail.com


class QLearner(object):
    def __init__(self,
                 num_states=100,
                 num_actions=4,
                 alpha=0.2,
                 gamma=0.9,
                 epsilon=0.5,
                 epsilon_decay=0.99):
        self.num_states = num_states
        self.num_actions = num_actions
        self.alpha = alpha
        self.gamma = gamma
        self.epsilon = epsilon
        self.epsilon_decay = epsilon_decay
        self.state = 0
        self.action = 0
        self.qtable = np.random.uniform(low=-1, high=1, size=(num_states, num_actions))

    def set_initial_state(self, state):
        """
        @summary: Sets the initial state and returns an action
        @param state: The initial state
        @returns: The selected action
        """
        self.state = state
        self.action = self.qtable[state].argsort()[-1]
        return self.action

    def move(self, state_prime, reward):
        """
        @summary: Moves to the given state with given reward and returns action
        @param state_prime: The new state
        @param reward: The reward
        @returns: The selected action
        """
        alpha = self.alpha
        gamma = self.gamma
        state = self.state
        action = self.action
        qtable = self.qtable

        choose_random_action = (1 - self.epsilon) <= np.random.uniform(0, 1)

        if choose_random_action:
            action_prime = random.randint(0, self.num_actions - 1)
        else:
            action_prime = self.qtable[state_prime].argsort()[-1]

        self.epsilon *= self.epsilon_decay

        qtable[state, action] = (1 - alpha) * qtable[state, action] + alpha * (reward + gamma * qtable[state_prime, action_prime])

        self.state = state_prime
        self.action = action_prime

        return self.action


def cart_pole_with_qlearning():
    env = gym.make('CartPole-v0')

    goal_average_steps = 195
    max_number_of_steps = 200
    number_of_iterations_to_average = 100

    number_of_features = env.observation_space.shape[0]
    last_time_steps = np.ndarray(0)

    cart_position_bins = pd.cut([-2.4, 2.4], bins=10, retbins=True)[1][1:-1]
    pole_angle_bins = pd.cut([-0.5, 0.5], bins=10, retbins=True)[1][1:-1]
    cart_velocity_bins = pd.cut([-2, 2], bins=10, retbins=True)[1][1:-1]
    angle_rate_bins = pd.cut([-4, 4], bins=10, retbins=True)[1][1:-1]

    list_reward = []
    list_i = []

    def build_state(features):
        return int("".join(map(lambda feature: str(int(feature)), features)))

    def to_bin(value, bins):
        return np.digitize(x=[value], bins=bins)[0]

    learner = QLearner(num_states=10 ** number_of_features,
                       num_actions=env.action_space.n,
                       alpha=0.2,
                       gamma=0.1,
                       epsilon=0.5,
                       epsilon_decay=0.99)

    for episode in xrange(50000):
        observation = env.reset()
        cart_position, cart_velocity, pole_angle, angle_rate_of_change = observation
        state = build_state([to_bin(cart_position, cart_position_bins),
                             to_bin(pole_angle, pole_angle_bins),
                             to_bin(cart_velocity, cart_velocity_bins),
                             to_bin(angle_rate_of_change, angle_rate_bins)])
        action = learner.set_initial_state(state)

        for step in xrange(max_number_of_steps - 1):
            env.render()
            observation, reward, done, info = env.step(action)

            cart_position, cart_velocity, pole_angle, angle_rate_of_change = observation

            state_prime = build_state([to_bin(cart_position, cart_position_bins),
                                       to_bin(pole_angle, pole_angle_bins),
                                       to_bin(cart_velocity, cart_velocity_bins),
                                       to_bin(angle_rate_of_change, angle_rate_bins)])

            if done:
                reward = -100

            action = learner.move(state_prime, reward)

            if done:
                last_time_steps = np.append(last_time_steps, [int(step + 1)])
                if len(last_time_steps) > number_of_iterations_to_average:
                    last_time_steps = np.delete(last_time_steps, 0)
                break
        if last_time_steps.mean() > goal_average_steps:
            print "Goal reached!"
            print "Episodes before solve: ", episode + 1
            print u"Best 100-episode performance {} {} {}".format(last_time_steps.max(),
                                                                  unichr(177),  # plus minus sign
                                                                  last_time_steps.std())
            break


if __name__ == "__main__":
    cart_pole_with_qlearning()