def test_openai_gym(self):
self.start_tests(name='openai-gym')
# state: box, action: discrete
self.unittest(environment=dict(environment='gym', level='CartPole-v0'),
num_episodes=2)
# state: discrete, action: box
# self.unittest(environment=dict(environment='gym', level='GuessingGame'), num_episodes=2)
# state: discrete, action: tuple(discrete)
# from gym.envs.algorithmic import ReverseEnv
# self.unittest(environment=ReverseEnv, num_episodes=2)
# state: discrete, action: discrete
from gym.envs.toy_text import FrozenLakeEnv
self.unittest(environment=FrozenLakeEnv, num_episodes=2)
# state: tuple, action: discrete
from gym.envs.toy_text import BlackjackEnv
self.unittest(environment=BlackjackEnv(), num_episodes=2)
# Classic control
self.unittest(environment='CartPole-v1', num_episodes=2)
self.unittest(environment='MountainCar-v0', num_episodes=2)
self.unittest(environment='MountainCarContinuous-v0', num_episodes=2)
self.unittest(environment='Pendulum-v1', num_episodes=2)
self.unittest(environment='Acrobot-v1', num_episodes=2)
# Box2d
self.unittest(environment='LunarLander-v2', num_episodes=2)
self.unittest(environment='LunarLanderContinuous-v2', num_episodes=2)
self.unittest(environment='BipedalWalker-v3', num_episodes=2)
self.unittest(environment='BipedalWalkerHardcore-v3', num_episodes=2)
# below: self.unittest(environment='CarRacing-v0', num_episodes=2)
# Toy text
# above: self.unittest(environment='Blackjack-v1', num_episodes=2)
self.unittest(environment='FrozenLake-v1', num_episodes=2)
self.unittest(environment='FrozenLake8x8-v1', num_episodes=2)
self.unittest(environment='CliffWalking-v0', num_episodes=2)
self.unittest(environment='Taxi-v3', num_episodes=2)
# Unit test
self.unittest(environment='CubeCrash-v0', num_episodes=2)
self.unittest(environment='CubeCrashSparse-v0', num_episodes=2)
self.unittest(environment='CubeCrashScreenBecomesBlack-v0',
num_episodes=2)
self.unittest(environment='MemorizeDigits-v0', num_episodes=2)
def test_openai_gym(self):
self.start_tests(name='openai-gym')
# state: box, action: discrete
self.unittest(environment=dict(environment='gym', level='CartPole-v0'))
# state: discrete, action: box
self.unittest(environment=dict(
environment='gym', level='GuessingGame', max_episode_steps=False))
# state: discrete, action: tuple(discrete)
from gym.envs.algorithmic import ReverseEnv
self.unittest(environment=ReverseEnv)
# state: tuple, action: discrete
from gym.envs.toy_text import BlackjackEnv
self.unittest(environment=BlackjackEnv())
# Find all states the we've visited in this episode
# We convert each state to a tuple so that we can use it as a dict key
states_in_episode = set([tuple(x[0]) for x in episode])
for state in states_in_episode:
# Find the first occurance of the state in the episode
first_occurence_idx = next(i for i, x in enumerate(episode)
if x[0] == state)
# Sum up all rewards since the first occurance
G = sum([
x[2] * (discount_factor**i)
for i, x in enumerate(episode[first_occurence_idx:])
])
# Calculate average return for this state over all sampled episodes
returns_sum[state] += G
returns_count[state] += 1.0
V[state] = returns_sum[state] / returns_count[state]
return V
if __name__ == "__main__":
# matplotlib.style.use('ggplot')
env = BlackjackEnv()
V_10k = mc_prediction(sample_policy, env, num_episodes=100000)
print(V_10k)
plot_value_function(V_10k, title="10,000 Steps")
# V_500k = mc_prediction(sample_policy, env, num_episodes=50000)
# plotting.plot_value_function(V_500k, title="500,000 Steps")
def test_openai_gym(self):
self.start_tests(name='openai-gym')
# state: box, action: discrete
self.unittest(environment=dict(environment='gym', level='CartPole-v0'),
num_episodes=2)
# state: discrete, action: box
self.unittest(environment=dict(environment='gym',
level='GuessingGame'),
num_episodes=2)
# state: discrete, action: tuple(discrete)
from gym.envs.algorithmic import ReverseEnv
self.unittest(environment=ReverseEnv, num_episodes=2)
# state: tuple, action: discrete
from gym.envs.toy_text import BlackjackEnv
self.unittest(environment=BlackjackEnv(), num_episodes=2)
# Classic control
# above: self.unittest(environment='CartPole-v1', num_episodes=2)
self.unittest(environment='MountainCar-v0', num_episodes=2)
self.unittest(environment='MountainCarContinuous-v0', num_episodes=2)
self.unittest(environment='Pendulum-v0', num_episodes=2)
self.unittest(environment='Acrobot-v1', num_episodes=2)
# Box2d
self.unittest(environment='LunarLander-v2', num_episodes=2)
self.unittest(environment='LunarLanderContinuous-v2', num_episodes=2)
self.unittest(environment='BipedalWalker-v3', num_episodes=2)
self.unittest(environment='BipedalWalkerHardcore-v3', num_episodes=2)
# below: self.unittest(environment='CarRacing-v0', num_episodes=2)
# Toy text
# above: self.unittest(environment='Blackjack-v0', num_episodes=2)
self.unittest(environment='KellyCoinflip-v0', num_episodes=2)
# TODO: out-of-bounds problems!
# self.unittest(environment=dict(
# environment='KellyCoinflipGeneralized-v0', clip_distributions=True
# ), num_episodes=2)
self.unittest(environment='FrozenLake-v0', num_episodes=2)
self.unittest(environment='FrozenLake8x8-v0', num_episodes=2)
self.unittest(environment='CliffWalking-v0', num_episodes=2)
self.unittest(environment='NChain-v0', num_episodes=2)
self.unittest(environment='Roulette-v0', num_episodes=2)
self.unittest(environment='Taxi-v3', num_episodes=2)
# above: self.unittest(environment='GuessingGame-v0', num_episodes=2)
self.unittest(environment='HotterColder-v0', num_episodes=2)
# Algorithmic
self.unittest(environment='Copy-v0', num_episodes=2)
self.unittest(environment='RepeatCopy-v0', num_episodes=2)
self.unittest(environment='ReversedAddition-v0', num_episodes=2)
self.unittest(environment='ReversedAddition3-v0', num_episodes=2)
self.unittest(environment='DuplicatedInput-v0', num_episodes=2)
# above: self.unittest(environment='Reverse-v0', num_episodes=2)
# Unit test
self.unittest(environment='CubeCrash-v0', num_episodes=2)
self.unittest(environment='CubeCrashSparse-v0', num_episodes=2)
self.unittest(environment='CubeCrashScreenBecomesBlack-v0',
num_episodes=2)
self.unittest(environment='MemorizeDigits-v0', num_episodes=2)
def __init__(self):
env = BlackjackEnv()
super().__init__(env)
self.observation_space = spaces.Discrete(704)