from typing import List, Dict, Union, Optional, Any
import iteround
import numpy as np
from numpy.typing import NDArray
from csle_common.dao.training.policy import Policy
from csle_common.dao.training.linear_threshold_stopping_policy import LinearThresholdStoppingPolicy
from csle_common.dao.training.tabular_policy import TabularPolicy
from csle_common.dao.training.agent_type import AgentType
from csle_common.dao.simulation_config.state import State
from csle_common.dao.training.player_type import PlayerType
from csle_common.dao.simulation_config.action import Action
from csle_common.dao.training.experiment_config import ExperimentConfig
from csle_common.dao.training.policy_type import PolicyType
[docs]class LinearTabularPolicy(Policy):
"""
A linear tabular policy that uses a linear threshold line to decide when to take action and a tabular policy to
decide which action to take
"""
def __init__(self, stopping_policy: LinearThresholdStoppingPolicy, action_policy: TabularPolicy,
simulation_name: str, states: List[State], player_type: PlayerType,
actions: List[Action], experiment_config: Optional[ExperimentConfig], avg_R: float,
agent_type: AgentType) -> None:
"""
Initializes the policy
:param simulation_name: the simulation name
:param attacker: whether it is an attacker or not
:param L: the number of stop actions
:param states: list of states (required for computing stage policies)
:param actions: list of actions
:param experiment_config: the experiment configuration used for training the policy
:param avg_R: the average reward of the policy when evaluated in the simulation
:param agent_type: the agent type
:param opponent_strategy: optionally an opponent strategy
"""
super(LinearTabularPolicy, self).__init__(agent_type=agent_type, player_type=player_type)
self.stopping_policy = stopping_policy
self.action_policy = action_policy
self.id = -1
self.simulation_name = simulation_name
self.states = states
self.actions = actions
self.experiment_config = experiment_config
self.avg_R = avg_R
self.policy_type = PolicyType.LINEAR_TABULAR
[docs] def action(self, o: List[float], deterministic: bool = True) -> Union[int, List[int], float, NDArray[Any]]:
"""
Multi-threshold stopping policy
:param o: the current observation
:param deterministic: boolean flag indicating whether the action selection should be deterministic
:return: the selected action
"""
stop = self.stopping_policy.action(o=o[1:])
if stop == 1:
return self.action_policy.action(o=int(o[0]))
else:
return 0
[docs] def probability(self, o: List[float], a: int) -> int:
"""
Probability of a given action
:param o: the current observation
:param a: a given action
:return: the probability of a
"""
taken_action = self.action(o=o)
return taken_action == a
[docs] def stage_policy(self, o: Any) -> Any:
"""
Gets the stage policy, i.e a |S|x|A| policy
:param o: the latest observation
:return: the |S|x|A| stage policy
"""
stage_strategy = np.zeros((len(self.states), len(self.actions)))
for i, s_a in enumerate(self.states):
o[0] = s_a
for j, a in enumerate(self.actions):
stage_strategy[i][j] = self.probability(o=o, a=j)
stage_strategy[i] = iteround.saferound(stage_strategy[i], 2)
assert round(sum(stage_strategy[i]), 3) == 1
return stage_strategy.tolist()
[docs] def to_dict(self) -> Dict[str, List[float]]:
"""
:return: a dict representation of the policy
"""
d: Dict[str, Any] = {}
d["stopping_policy"] = self.stopping_policy.to_dict()
d["action_policy"] = self.action_policy.to_dict()
d["id"] = self.id
d["simulation_name"] = self.simulation_name
d["states"] = list(map(lambda x: x.to_dict(), self.states))
d["actions"] = list(map(lambda x: x.to_dict(), self.actions))
d["player_type"] = self.player_type
d["agent_type"] = self.agent_type
if self.experiment_config is not None:
d["experiment_config"] = self.experiment_config.to_dict()
else:
d["experiment_config"] = None
d["avg_R"] = self.avg_R
d["policy_type"] = self.policy_type
return d
[docs] @staticmethod
def from_dict(d: Dict[str, Any]) -> "LinearTabularPolicy":
"""
Converst a dict representation of the object to an instance
:param d: the dict to convert
:return: the created instance
"""
obj = LinearTabularPolicy(
stopping_policy=LinearThresholdStoppingPolicy.from_dict(d["stopping_policy"]),
action_policy=TabularPolicy.from_dict(d["action_policy"]),
simulation_name=d["simulation_name"],
states=list(map(lambda x: State.from_dict(x), d["states"])), player_type=d["player_type"],
actions=list(map(lambda x: Action.from_dict(x), d["actions"])),
experiment_config=ExperimentConfig.from_dict(d["experiment_config"]), avg_R=d["avg_R"],
agent_type=d["agent_type"])
obj.id = d["id"]
return obj
def __str__(self) -> str:
"""
:return: a string representation of the object
"""
return f"stopping_policy: {self.stopping_policy}, action_policy: {self.action_policy}, " \
f"id: {self.id}, simulation_name: {self.simulation_name}, " \
f"player_type: {self.player_type}, " \
f"states: {self.states}, agent_type: {self.agent_type}, actions: {self.actions}," \
f"experiment_config: {self.experiment_config}, avg_R: {self.avg_R}, policy_type: {self.policy_type}"
[docs] @staticmethod
def from_json_file(json_file_path: str) -> "LinearTabularPolicy":
"""
Reads a json file and converts it to a DTO
:param json_file_path: the json file path
:return: the converted DTO
"""
import io
import json
with io.open(json_file_path, 'r') as f:
json_str = f.read()
return LinearTabularPolicy.from_dict(json.loads(json_str))
[docs] def copy(self) -> "LinearTabularPolicy":
"""
:return: a copy of the DTO
"""
return self.from_dict(self.to_dict())