def get_environment(self):
"""
Return Sigmoid env with current configuration
Returns
-------
SigmoidEnv
Sigmoid environment
"""
if "instance_set" not in self.config.keys():
self.read_instance_set()
# Read test set if path is specified
if "test_set" not in self.config.keys() and "test_set_path" in self.config.keys():
self.read_instance_set(test=True)
if (
"env_type" in self.config
): # The env_type determines which Sigmoid environment to use.
if self.config["env_type"].lower() in [
"continuous",
"cont",
]: # Either continuous ...
if (
self.config["action_space"] == "Box"
): # ... in both actions and x-axis state, only ...
env = ContinuousSigmoidEnv(self.config)
elif (
self.config["action_space"] == "Discrete"
): # ... continuous in the x-axis state or ...
env = ContinuousStateSigmoidEnv(self.config)
else:
raise Exception(
f'The given environment type "{self.config["env_type"]}" does not support the'
f' chosen action_space "{self.config["action_space"]}". The action space has to'
f' be either of type "Box" for continuous actions or "Discrete".'
)
else: # ... discrete.
env = SigmoidEnv(self.config)
else: # If the type is not specified we the simplest, fully discrete version.
env = SigmoidEnv(self.config)
for func in self.wrap_funcs:
env = func(env)
return env
def get_environment(self):
"""
Return Sigmoid env with current configuration
Returns
-------
SigmoidEnv
Sigmoid environment
"""
if "instance_set" not in self.config.keys():
self.read_instance_set()
return SigmoidEnv(self.config)
def get_benchmark(self, dimension=None, seed=0):
"""
Get Benchmark from DAC paper
Parameters
-------
dimension : int
Sigmoid dimension, was 1, 2, 3 or 5 in the paper
seed : int
Environment seed
Returns
-------
env : SigmoidEnv
Sigmoid environment
"""
self.config = objdict(SIGMOID_DEFAULTS.copy())
if dimension == 1:
self.set_action_values([3])
if dimension == 2:
self.set_action_values([3, 3])
if dimension == 3:
self.set_action_values((3, 3, 3))
if dimension == 5:
self.set_action_values((3, 3, 3, 3, 3))
self.config.seed = seed
self.config.instance_set = [0]
env = SigmoidEnv(self.config)
def sample_sigmoid():
rng = np.random.default_rng()
shifts = rng.normal(
self.config.cutoff / 2,
self.config.cutoff / 4,
self.config.action_space_args[0],
)
slopes = (
rng.choice([-1, 1], self.config.action_space_args[0])
* rng.uniform(size=self.config.action_space_args[0])
* self.config.slope_multiplier
)
return np.concatenate((shifts, slopes))
sampling_env = InstanceSamplingWrapper(env, sampling_function=sample_sigmoid)
return sampling_env
def get_environment(self):
"""
Return Sigmoid env with current configuration
Returns
-------
SigmoidEnv
Sigmoid environment
"""
if "instance_set" not in self.config.keys():
self.read_instance_set()
env = SigmoidEnv(self.config)
for func in self.wrap_funcs:
env = func(env)
return env
def get_benchmark(self, dimension=None, seed=0):
"""
Get Benchmark from DAC paper
Parameters
-------
dimension : int
Sigmoid dimension, was 1, 2, 3 or 5 in the paper
seed : int
Environment seed
Returns
-------
env : SigmoidEnv
Sigmoid environment
"""
self.config = objdict(SIGMOID_DEFAULTS.copy())
if dimension == 1:
self.set_action_values([3])
self.config.instance_set_path = (
"../instance_sets/sigmoid/sigmoid_1D3M_train.csv")
self.config.benchmark_info["state_description"] = [
"Remaining Budget",
"Shift (dimension 1)",
"Slope (dimension 1)",
"Action",
]
if dimension == 2:
self.set_action_values([3, 3])
if dimension == 3:
self.set_action_values((3, 3, 3))
self.config.instance_set_path = (
"../instance_sets/sigmoid/sigmoid_3D3M_train.csv")
self.config.benchmark_info["state_description"] = [
"Remaining Budget",
"Shift (dimension 1)",
"Slope (dimension 1)",
"Shift (dimension 2)",
"Slope (dimension 2)",
"Shift (dimension 3)",
"Slope (dimension 3)",
"Action 1",
"Action 2",
"Action 3",
]
if dimension == 5:
self.set_action_values((3, 3, 3, 3, 3))
self.config.instance_set_path = (
"../instance_sets/sigmoid/sigmoid_5D3M_train.csv")
self.config.benchmark_info["state_description"] = [
"Remaining Budget",
"Shift (dimension 1)",
"Slope (dimension 1)",
"Shift (dimension 2)",
"Slope (dimension 2)",
"Shift (dimension 3)",
"Slope (dimension 3)",
"Shift (dimension 4)",
"Slope (dimension 4)",
"Shift (dimension 5)",
"Slope (dimension 5)",
"Action 1",
"Action 2",
"Action 3",
"Action 4",
"Action 5",
]
self.config.seed = seed
self.read_instance_set()
env = SigmoidEnv(self.config)
return env
def make_env(self):
config = SIGMOID_DEFAULTS
config["instance_set"] = {20: [0, 1, 2, 3]}
env = SigmoidEnv(config)
return env