def __init__(self, n=5, slip=0.2, small=2, large=10):
self.n = n
self.slip = slip # probability of 'slipping' an action
self.small = small # payout for 'backwards' action
self.large = large # payout at end of chain for 'forwards' action
self.state = 0 # Start at beginning of the chain
self.action_space = spaces.Discrete(2)
self.observation_space = spaces.Discrete(self.n)
self.seed()
def __init__(self, nS, nA, P, isd):
self.P = P
self.isd = isd
self.lastaction = None # for rendering
self.nS = nS
self.nA = nA
self.action_space = spaces.Discrete(self.nA)
self.observation_space = spaces.Discrete(self.nS)
self.seed()
self.reset()
def __init__(self,
initialWealth=25.0,
edgePriorAlpha=7,
edgePriorBeta=3,
maxWealthAlpha=5.0,
maxWealthM=200.0,
maxRoundsMean=300.0,
maxRoundsSD=25.0,
reseed=True):
# store the hyperparameters for passing back into __init__() during resets so the same hyperparameters govern the next game's parameters, as the user expects: TODO: this is boilerplate, is there any more elegant way to do this?
self.initialWealth = float(initialWealth)
self.edgePriorAlpha = edgePriorAlpha
self.edgePriorBeta = edgePriorBeta
self.maxWealthAlpha = maxWealthAlpha
self.maxWealthM = maxWealthM
self.maxRoundsMean = maxRoundsMean
self.maxRoundsSD = maxRoundsSD
# draw this game's set of parameters:
edge = prng.np_random.beta(edgePriorAlpha, edgePriorBeta)
maxWealth = round(
genpareto.rvs(maxWealthAlpha,
maxWealthM,
random_state=prng.np_random))
maxRounds = int(
round(prng.np_random.normal(maxRoundsMean, maxRoundsSD)))
# add an additional global variable which is the sufficient statistic for the Pareto distribution on wealth cap;
# alpha doesn't update, but x_m does, and simply is the highest wealth count we've seen to date:
self.maxEverWealth = float(self.initialWealth)
# for the coinflip edge, it is total wins/losses:
self.wins = 0
self.losses = 0
# for the number of rounds, we need to remember how many rounds we've played:
self.roundsElapsed = 0
# the rest proceeds as before:
self.action_space = spaces.Discrete(int(maxWealth * 100))
self.observation_space = spaces.Tuple((
spaces.Box(0, maxWealth, shape=[1],
dtype=np.float32), # current wealth
spaces.Discrete(maxRounds + 1), # rounds elapsed
spaces.Discrete(maxRounds + 1), # wins
spaces.Discrete(maxRounds + 1), # losses
spaces.Box(0, maxWealth, [1],
dtype=np.float32))) # maximum observed wealth
self.reward_range = (0, maxWealth)
self.edge = edge
self.wealth = self.initialWealth
self.maxRounds = maxRounds
self.rounds = self.maxRounds
self.maxWealth = maxWealth
if reseed or not hasattr(self, 'np_random'): self.seed()
def __init__(self, natural=False):
self.action_space = spaces.Discrete(2)
self.observation_space = spaces.Tuple((
spaces.Discrete(32),
spaces.Discrete(11),
spaces.Discrete(2)))
self.seed()
# Flag to payout 1.5 on a "natural" blackjack win, like casino rules
# Ref: http://www.bicyclecards.com/how-to-play/blackjack/
self.natural = natural
# Start the first game
self.reset()
def __init__(self):
self.gravity = 9.8
self.masscart = 1.0
self.masspole = 0.1
self.total_mass = (self.masspole + self.masscart)
self.length = 0.5 # actually half the pole's length
self.polemass_length = (self.masspole * self.length)
self.force_mag = 10.0
self.tau = 0.02 # seconds between state updates
self.kinematics_integrator = 'euler'
# Angle at which to fail the episode
self.theta_threshold_radians = 12 * 2 * math.pi / 360
self.x_threshold = 2.4
# Angle limit set to 2 * theta_threshold_radians so failing observation is still within bounds
high = np.array([
self.x_threshold * 2,
np.finfo(np.float32).max, self.theta_threshold_radians * 2,
np.finfo(np.float32).max
])
self.action_space = spaces.Discrete(2)
self.observation_space = spaces.Box(-high, high, dtype=np.float32)
self.seed()
self.viewer = None
self.state = None
self.steps_beyond_done = None
def __init__(self):
self.seed()
self.viewer = None
self.observation_space = spaces.Box(0,
255, (FIELD_H, FIELD_W, 3),
dtype=np.uint8)
self.action_space = spaces.Discrete(3)
self.reset()
def __init__(self):
self.viewer = None
high = np.array([1.0, 1.0, 1.0, 1.0, self.MAX_VEL_1, self.MAX_VEL_2])
low = -high
self.observation_space = spaces.Box(low=low,
high=high,
dtype=np.float32)
self.action_space = spaces.Discrete(3)
self.state = None
self.seed()
def __init__(self,
initialWealth=25.0,
edge=0.6,
maxWealth=250.0,
maxRounds=300):
self.action_space = spaces.Discrete(int(
maxWealth * 100)) # betting in penny increments
self.observation_space = spaces.Tuple((
spaces.Box(0, maxWealth, [1], dtype=np.float32), # (w,b)
spaces.Discrete(maxRounds + 1)))
self.reward_range = (0, maxWealth)
self.edge = edge
self.wealth = initialWealth
self.initialWealth = initialWealth
self.maxRounds = maxRounds
self.maxWealth = maxWealth
self.seed()
self.reset()
def __init__(self):
self.seed()
self.viewer = None
self.observation_space = spaces.Box(0,
255, (FIELD_H, FIELD_W, 3),
dtype=np.uint8)
self.action_space = spaces.Discrete(10)
self.bogus_mnist = np.zeros((10, 6, 6), dtype=np.uint8)
for digit in range(10):
for y in range(6):
self.bogus_mnist[digit, y, :] = [
ord(char) for char in bogus_mnist[digit][y]
]
self.reset()
def __init__(self):
self.range = 1000 # Randomly selected number is within +/- this value
self.bounds = 10000
self.action_space = spaces.Box(low=np.array([-self.bounds]),
high=np.array([self.bounds]),
dtype=np.float32)
self.observation_space = spaces.Discrete(4)
self.number = 0
self.guess_count = 0
self.guess_max = 200
self.observation = 0
self.seed()
self.reset()
def __init__(self):
self.range = 1000 # +/- value the randomly select number can be between
self.bounds = 2000 # Action space bounds
self.action_space = spaces.Box(low=np.array([-self.bounds]),
high=np.array([self.bounds]),
dtype=np.float32)
self.observation_space = spaces.Discrete(4)
self.number = 0
self.guess_count = 0
self.guess_max = 200
self.observation = 0
self.seed()
self.reset()
def __init__(self):
self.min_position = -1.2
self.max_position = 0.6
self.max_speed = 0.07
self.goal_position = 0.5
self.low = np.array([self.min_position, -self.max_speed])
self.high = np.array([self.max_position, self.max_speed])
self.viewer = None
self.action_space = spaces.Discrete(3)
self.observation_space = spaces.Box(self.low,
self.high,
dtype=np.float32)
self.seed()
self.reset()
def __init__(self, spots=37):
self.n = spots + 1
self.action_space = spaces.Discrete(self.n)
self.observation_space = spaces.Discrete(1)
self.seed()