taxi-v2.py

import numpy as np



import gym



env = gym.make("Taxi-v2").env


env.reset() # reset environment to a new, random state
env.render()

print("Action Space {}".format(env.action_space))
print("State Space {}".format(env.observation_space))

state = env.encode(3, 1, 2, 0) # (taxi row, taxi column, passenger index, destination index)
print("State:", state)

env.s = state
env.render()


print(env.P[329])


q_table = np.zeros([env.observation_space.n, env.action_space.n])

"""Training the agent"""

import random
from IPython.display import clear_output

# Hyperparameters
alpha = 0.1
gamma = 0.6
epsilon = 0.1

# For plotting metrics
all_epochs = []
all_penalties = []

for i in range(1, 100001):
    state = env.reset()

    epochs, penalties, reward, = 0, 0, 0
    done = False
    
    while not done:
        if random.uniform(0, 1) < epsilon:
            action = env.action_space.sample() # Explore action space
        else:
            action = np.argmax(q_table[state]) # Exploit learned values

        next_state, reward, done, info = env.step(action) 
        
        old_value = q_table[state, action]
        next_max = np.max(q_table[next_state])
        
        new_value = (1 - alpha) * old_value + alpha * (reward + gamma * next_max)
        q_table[state, action] = new_value

        if reward == -10:
            penalties += 1

        state = next_state
        epochs += 1
        
    if i % 100 == 0:
        clear_output(wait=True)
        print("Episode: %d" %i)

print("Training finished.\n")


print(q_table[329])