TaxiDriverGym introduction

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import gymnasium as gym

import gymnasium as gym

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env = gym.make("Taxi-v3", render_mode='ansi').env

env = gym.make("Taxi-v3", render_mode='ansi').env

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

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

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state = env.reset()
print(state[0])
print(env.render())

state = env.reset() print(state[0]) print(env.render())

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# escolhe uma acao aleatoria
action = env.action_space.sample()
print(action)

# escolhe uma acao aleatoria action = env.action_space.sample() print(action)

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# executa a acao
state, reward, done, truncated, info = env.step(action)
print(state)
print(env.render())

# executa a acao state, reward, done, truncated, info = env.step(action) print(state) print(env.render())

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# executa a acao ir para north
state, reward, done, truncated, info = env.step(1)
print(env.render())

# executa a acao ir para north state, reward, done, truncated, info = env.step(1) print(env.render())

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state, reward, done, truncated, info = env.step(0)
print(env.render())

state, reward, done, truncated, info = env.step(0) print(env.render())

actions: 0 = south 1 = north 2 = east 3 = west 4 = pickup 5 = dropoff

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env.close()

env.close()