def from_values(cls, values: dict):
mapping = dict()
for s in State.get_all_states():
if s.current_sum < 12: mapping[s] = Action.HIT
elif values[StateActionPair(s, Action.STICK)] > values[StateActionPair(s, Action.HIT)]:
mapping[s] = Action.STICK
else: mapping[s] = Action.HIT
return Policy.from_deterministic_mapping(mapping)
def epsilon_greedy_from_values(cls, values: dict, exploring_prob: Callable):
mapping = dict()
for s in State.get_all_states():
if values[StateActionPair(s, Action.STICK)] > values[StateActionPair(s, Action.HIT)]:
mapping[s] = [1. - exploring_prob(), exploring_prob()]
else:
mapping[s] = [exploring_prob(), 1. - exploring_prob()]
return Policy.from_probabilistic_mapping(mapping)
from itertools import product
from model.actions import Action
from model.policy import Policy
from model.state import State, StateActionPair
ALL_STATES = State.get_all_states()
ALL_STATE_ACTION_PAIRS = [
StateActionPair(s, a) for s, a in product(ALL_STATES, list(Action))
]
class Algorithm:
@classmethod
def _create_sap_unif_mapping(cls, value):
return {sap: value for sap in ALL_STATE_ACTION_PAIRS}
@property
def policy(self) -> Policy:
raise NotImplemented
def __init__(self):
self._Q = Algorithm._create_sap_unif_mapping(0.)
def train(self, rounds: int) -> None:
raise NotImplemented
class MonteCarloAlgorithm(Algorithm):
def __init__(self):
super().__init__()
