utils.py

import base64
import random
from itertools import zip_longest

import imageio
import IPython
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import numpy as np
import pandas as pd
import tensorflow as tf
from statsmodels.iolib.table import SimpleTable


SEED = 0              # seed for pseudo-random number generator
MINIBATCH_SIZE = 64   # mini-batch size
TAU = 1e-3            # soft update parameter
E_DECAY = 0.995       # ε decay rate for ε-greedy policy
E_MIN = 0.01          # minimum ε value for ε-greedy policy


random.seed(SEED)


def get_experiences(memory_buffer):
    experiences = random.sample(memory_buffer, k=MINIBATCH_SIZE)
    states = tf.convert_to_tensor(np.array([e.state for e in experiences if e is not None]),dtype=tf.float32)
    actions = tf.convert_to_tensor(np.array([e.action for e in experiences if e is not None]), dtype=tf.float32)
    rewards = tf.convert_to_tensor(np.array([e.reward for e in experiences if e is not None]), dtype=tf.float32)
    next_states = tf.convert_to_tensor(np.array([e.next_state for e in experiences if e is not None]),dtype=tf.float32)
    done_vals = tf.convert_to_tensor(np.array([e.done for e in experiences if e is not None]).astype(np.uint8),
                                     dtype=tf.float32)
    return (states, actions, rewards, next_states, done_vals)


def check_update_conditions(t, num_steps_upd, memory_buffer):
    if (t + 1) % num_steps_upd == 0 and len(memory_buffer) > MINIBATCH_SIZE:
        return True
    else:
        return False
    
    
def get_new_eps(epsilon):
    return max(E_MIN, E_DECAY*epsilon)


def get_action(q_values, epsilon=0):
    if random.random() > epsilon:
        return np.argmax(q_values.numpy()[0])
    else:
        return random.choice(np.arange(4))
    
    
def update_target_network(q_network, target_q_network):
    for target_weights, q_net_weights in zip(target_q_network.weights, q_network.weights):
        target_weights.assign(TAU * q_net_weights + (1.0 - TAU) * target_weights)
    

def plot_history(reward_history, rolling_window=20, lower_limit=None,
                 upper_limit=None, plot_rw=True, plot_rm=True):
    
    if lower_limit is None or upper_limit is None:
        rh = reward_history
        xs = [x for x in range(len(reward_history))]
    else:
        rh = reward_history[lower_limit:upper_limit]
        xs = [x for x in range(lower_limit,upper_limit)]
    
    df = pd.DataFrame(rh)
    rollingMean = df.rolling(rolling_window).mean()

    plt.figure(figsize=(10,7), facecolor='white')
    
    if plot_rw:
        plt.plot(xs, rh, linewidth=1, color='cyan')
    if plot_rm:
        plt.plot(xs, rollingMean, linewidth=2, color='magenta')

    text_color = 'black'
        
    ax = plt.gca()
    ax.set_facecolor('black')
    plt.grid()
#     plt.title("Total Point History", color=text_color, fontsize=40)
    plt.xlabel('Episode', color=text_color, fontsize=30)
    plt.ylabel('Total Points', color=text_color, fontsize=30)
    yNumFmt = mticker.StrMethodFormatter('{x:,}')
    ax.yaxis.set_major_formatter(yNumFmt)
    ax.tick_params(axis='x', colors=text_color)
    ax.tick_params(axis='y', colors=text_color)
    plt.show()
    
    
def display_table(initial_state, action, next_state, reward, done):

    action_labels = ["Do nothing", "Fire right engine", "Fire main engine", "Fire left engine"]
    
    # Do not use column headers
    column_headers = None

    with np.printoptions(formatter={'float': '{:.3f}'.format}):
        table_info = [("Initial State:", [f"{initial_state}"]),
                      ("Action:", [f"{action_labels[action]}"]),
                      ("Next State:", [f"{next_state}"]),
                      ("Reward Received:", [f"{reward:.3f}"]),
                      ("Episode Terminated:", [f"{done}"])]

    # Generate table  
    row_labels, data = zip_longest(*table_info)
    table = SimpleTable(data, column_headers, row_labels)

    return table


def embed_mp4(filename):
    """Embeds an mp4 file in the notebook."""
    video = open(filename,'rb').read()
    b64 = base64.b64encode(video)
    tag = '''
    <video width="840" height="480" controls>
    <source src="data:video/mp4;base64,{0}" type="video/mp4">
    Your browser does not support the video tag.
    </video>'''.format(b64.decode())
    return IPython.display.HTML(tag)
        
        
def create_video(filename, env, q_network, fps=30):
    with imageio.get_writer(filename, fps=fps) as video:
        done = False
        state = env.reset()
        frame = env.render(mode="rgb_array")
        video.append_data(frame)
        while not done:    
            state = np.expand_dims(state, axis=0)
            q_values = q_network(state)
            action = np.argmax(q_values.numpy()[0])
            state, _, done, _ = env.step(action)
            frame = env.render(mode="rgb_array")
            video.append_data(frame)