def __init__(self):
super().__init__()
self._max_cars: int = 20 # problem statement = 20
self._environment_parameters = EnvironmentParameters(
max_cars=self._max_cars,
extra_rules=True, # change this for extra rules in book as per challenge
)
self._comparison_settings = common.Settings(
gamma=0.9,
policy_parameters=common.PolicyParameters(
policy_type=common.PolicyType.TABULAR_DETERMINISTIC,
),
algorithm_parameters=common.AlgorithmParameters(
theta=0.1 # accuracy of policy_evaluation
),
display_every_step=True,
)
self._graph3d_values = common.Graph3DValues(
x_label="Cars at 1st location",
y_label="Cars at 2nd location",
z_label="V(s)",
x_min=0,
x_max=self._max_cars,
y_min=0,
y_max=self._max_cars,
)
self._grid_view_parameters = common.GridViewParameters(
grid_view_type=common.GridViewType.JACKS,
show_result=True,
show_policy=True,
)
class Settings(common.Settings):
gamma: float = 1.0
runs: int = 1
training_episodes: int = 500_000
episode_print_frequency: int = 10_000
policy_parameters: common.PolicyParameters = common.PolicyParameters(
policy_type=common.PolicyType.TABULAR_DETERMINISTIC, )
class Settings(common.Settings):
runs: int = 100
# runs_multiprocessing: common.ParallelContextType =common.ParallelContextType.FORK_GLOBAL
training_episodes: int = 100
policy_parameters: common.PolicyParameters = common.PolicyParameters(
policy_type=common.PolicyType.TABULAR_NONE)
algorithm_parameters: common.AlgorithmParameters = AlgorithmParameters()
def __init__(self, environment: TabularEnvironment,
policy_parameters: common.PolicyParameters):
super().__init__(environment, policy_parameters)
self.epsilon: float = self._policy_parameters.epsilon
greedy_policy_parameters = common.PolicyParameters(
policy_type=common.PolicyType.TABULAR_DETERMINISTIC,
store_matrix=False,
)
self.greedy_policy: Deterministic = Deterministic(
self._environment, greedy_policy_parameters)
class Settings(common.Settings):
gamma: float = 1.0 # 0.99999
policy_parameters: common.PolicyParameters = common.PolicyParameters(
policy_type=common.PolicyType.TABULAR_DETERMINISTIC, )
display_every_step: bool = False