Advanced Deep Learning with Keras

Deep Reinforcement Learning
This will help us to focus on building a working RL algorithm. To run the code in
slow motion or delay of 1 sec per action:
$ python3 q-frozenlake-9.5.1.py -d -t=1
Mode Run Approx % Goal
Train non-slippery python3 q-frozenlake-9.5.1.py 26.0
Test non-slippery python3 q-frozenlake-9.5.1.py -d 76.0
Pure random action python3 q-frozenlake-9.5.1.py -e 1.5
non-slippery
Train slippery python3 q-frozenlake-9.5.1.py -s 26
Test slippery python3 q-frozenlake-9.5.1.py -s -d 71.0
Pure random slippery python3 q-frozenlake-9.5.1.py -s -e 1.5
Table 9.5.1: Baseline and performance of generalized Q-Learning on the
FrozenLake-v0 environment with learning rate = 0.5
Listing 9.5.1, q-frozenlake-9.5.1.py shows the implementation of Q-Learning on
FrozenLake-v0 environment:
from collections import deque
import numpy as np
import argparse
import os
import time
import gym
from gym import wrappers, logger
class QAgent():
def __init__(self,
observation_space,
action_space,
demo=False,
slippery=False,
decay=0.99):
self.action_space = action_space
# number of columns is equal to number of actions
col = action_space.n
# number of rows is equal to number of states
row = observation_space.n
# build Q Table with row x col dims
self.q_table = np.zeros([row, col])
[ 290 ]