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%matplotlib inline

import gym
import itertools
import matplotlib
import numpy as np
import pandas as pd
import sys

if "../" not in sys.path:

from collections import defaultdict
from lib.envs.cliff_walking import CliffWalkingEnv
from lib import plotting'ggplot')

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env = CliffWalkingEnv()

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def make_epsilon_greedy_policy(Q, epsilon, nA):
    Creates an epsilon-greedy policy based on a given Q-function and epsilon.
        Q: A dictionary that maps from state -> action-values.
            Each value is a numpy array of length nA (see below)
        epsilon: The probability to select a random action . float between 0 and 1.
        nA: Number of actions in the environment.
        A function that takes the observation as an argument and returns
        the probabilities for each action in the form of a numpy array of length nA.
    def policy_fn(observation):
        A = np.ones(nA, dtype=float) * epsilon / nA
        best_action = np.argmax(Q[observation])
        A[best_action] += (1.0 - epsilon)
        return A
    return policy_fn

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def q_learning(env, num_episodes, discount_factor=1.0, alpha=0.5, epsilon=0.1):
    Q-Learning algorithm: Off-policy TD control. Finds the optimal greedy policy
    while following an epsilon-greedy policy
        env: OpenAI environment.
        num_episodes: Number of episodes to run for.
        discount_factor: Lambda time discount factor.
        alpha: TD learning rate.
        epsilon: Chance the sample a random action. Float betwen 0 and 1.
        A tuple (Q, episode_lengths).
        Q is the optimal action-value function, a dictionary mapping state -> action values.
        stats is an EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.
    # The final action-value function.
    # A nested dictionary that maps state -> (action -> action-value).
    Q = defaultdict(lambda: np.zeros(env.action_space.n))

    # Keeps track of useful statistics
    stats = plotting.EpisodeStats(
    # The policy we're following
    policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)
    for i_episode in range(num_episodes):
        # Print out which episode we're on, useful for debugging.
        if (i_episode + 1) % 100 == 0:
            print("\rEpisode {}/{}.".format(i_episode + 1, num_episodes), end="")
        # Reset the environment and pick the first action
        state = env.reset()
        # One step in the environment
        # total_reward = 0.0
        for t in itertools.count():
            # Take a step
            action_probs = policy(state)
            action = np.random.choice(np.arange(len(action_probs)), p=action_probs)
            next_state, reward, done, _ = env.step(action)

            # Update statistics
            stats.episode_rewards[i_episode] += reward
            stats.episode_lengths[i_episode] = t
            # TD Update
            best_next_action = np.argmax(Q[next_state])    
            td_target = reward + discount_factor * Q[next_state][best_next_action]
            td_delta = td_target - Q[state][action]
            Q[state][action] += alpha * td_delta
            if done:
            state = next_state
    return Q, stats

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Q, stats = q_learning(env, 500)

Episode 500/500.

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