Exercise TD q-learning with frozenlake from gym API
Table of Contents
In this notebook you will use Temporal-Difference learning (TD learning), especially the Q-learning algorithm to train the agent for the Frozenlake environment provided by the gym API. The main difference between TD learning and Monte Carlo methods is that instead of learning from whole episodes one goes even one more step back and learns based on a step by step approach.
To get familiar with the Frozenlake environment it is suggested to read through the first part of the notebook
exercise-monte-carlo-frozenlake-gym. Also the usage of tie-breaking argmax and policy performance testing is explaned their in more detail.
To solve this notebook you should aquire knowledge about Temporal-Difference learning, especially Q-learning, agent-environment interaction, state-action values and policy improvement.
SUT18 chapter 6 to gain knowledge about the mentioned topics and terms.
SUT18 is the standard literature for reinforcment learning and the basis for this and following notebooks.
import gym import numpy as np import matplotlib.pyplot as plt
env = gym.make('FrozenLake-v0')
def random_argmax_axis1(b): """ a random tie-breaking argmax""" return np.argmax(np.random.random(b.shape) * (b.T==b.max(axis=1)).T, axis=1)
##$ \epsilon $-greedy policy
def epsilon_policy(s,Q_sa,env,epsilon=0.2): if np.random.uniform(0,1) < epsilon: action = env.action_space.sample() #exploration else: action = random_argmax_axis1(Q_sa)[s] #explotation return action
Implement the Q-learning algorithm given in chapter 6.5 in
def q_learning(env,nb_episode,alpha=0.5,gamma=0.95): """ Args: env: given environment nb_episode: number of episodes used for training Kwargs: alpha: learning rate for Q-learning gamma: discount rate Return: Q_sa: learned Q-table """ Q_sa = np.zeros((env.observation_space.n,env.action_space.n)) #for e in range(nb_episode): #done = False #while not done: #generate steps #q_learning return NotImplementedError
Training the agent
Q_sa = q_learning(env,100000)
Test performance after training
Since each performance test might lead to a different value accordingly to the random nature of our frozenlake environment due to its slippery condition, it is usefull to find the mean over many tests. Reaching over 0.7 means your learning was succesful.
greedy_policy = random_argmax_axis1(Q_sa) print(greedy_policy)
policy = lambda s: greedy_policy[s]
def test_performance(policy, nb_episodes=100): sum_returns = 0 for i in range(nb_episodes): state = env.reset() done = False while not done: action = policy(state) state, reward, done, info = env.step(action) if done: sum_returns += reward return sum_returns/nb_episodes
mean_performance_list =  mean_performance = 0 n = 300 for i in range(n): performance = test_performance(policy) mean_performance_list.append(performance) mean_performance += performance print(mean_performance/n)
plt.hist(mean_performance_list,bins=30) plt.grid() plt.xlabel("Policy performance") plt.show()
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is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://gitlab.com/deep.TEACHING.
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Copyright 2019 Oliver Fischer
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