from typing import Union, List, Dict, Optional, Tuple, Any
import random
from csle_common.dao.training.agent_type import AgentType
from csle_common.dao.training.player_type import PlayerType
from csle_common.dao.training.policy import Policy
from csle_common.dao.simulation_config.action import Action
from csle_common.dao.training.policy_type import PolicyType
[docs]class RandomPolicy(Policy):
"""
Object representing a static policy
"""
def __init__(self, player_type: PlayerType, actions: List[Action],
stage_policy_tensor: Optional[List[List[float]]]) -> None:
"""
Initializes the policy
:param actions: list of actions
:param player_type: the player type
:param stage_policy_tensor: the stage policy tensor
"""
super(RandomPolicy, self).__init__(agent_type=AgentType.RANDOM, player_type=player_type)
self.actions = actions
self.stage_policy_tensor = stage_policy_tensor
self.policy_type = PolicyType.RANDOM
[docs] def action(self, o: Union[List[Union[int, float]], int, float], deterministic: bool = True) -> int:
"""
Selects the next action
:param o: the input observation
:param deterministic: boolean flag indicating whether the action selection should be deterministic
:return: the next action and its probability
"""
action = random.choice(self.actions)
return action.id
def _defender_action(self, o: Union[List[Union[int, float]], int, float]) -> Tuple[Union[int, float], float]:
"""
Selects the next defender action
:param o: the input observation
:return: the next action and its probability
"""
action = random.choice(self.actions)
return action.id, self.probability(o=o, a=action.id)
[docs] def probability(self, o: Union[List[Union[int, float]], int, float], a: int) -> float:
"""
Calculates the probability of taking a given action for a given observation
:param o: the input observation
:param a: the action
:return: p(a|o)
"""
return 1 / len(self.actions)
[docs] @staticmethod
def from_dict(d: Dict[str, Any]) -> "RandomPolicy":
"""
Converts a dict representation to an instance
:param d: the dict to convert
:return: the created instance
"""
return RandomPolicy(actions=list(map(lambda x: Action.from_dict(x), d["actions"])),
stage_policy_tensor=d["stage_policy_tensor"], player_type=d["player_type"])
[docs] def to_dict(self) -> Dict[str, Any]:
"""
:return: A dict representation of the function
"""
d: Dict[str, Any] = {}
d["agent_type"] = self.agent_type
d["player_type"] = self.player_type
d["actions"] = list(map(lambda x: x.to_dict(), self.actions))
d["stage_policy_tensor"] = self.stage_policy_tensor
d["policy_type"] = self.policy_type
return d
[docs] def stage_policy(self, o: Any) -> Union[List[List[float]], List[float]]:
"""
Gets the stage policy, i.e a |S|x|A| policy
:param o: the latest observation
:return: the |S|x|A| stage policy
"""
if self.stage_policy_tensor is None:
raise ValueError("Stage policy is None")
return self.stage_policy_tensor
def __str__(self) -> str:
"""
:return: a string representation of the policy
"""
return f"agent_type: {self.agent_type}, player_type: {self.player_type}, " \
f"actions: {list(map(lambda x: str(x), self.actions))}, " \
f"stage_policy_tensor: {self.stage_policy_tensor}, policy_type: {self.policy_type}"
[docs] @staticmethod
def from_json_file(json_file_path: str) -> "RandomPolicy":
"""
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 RandomPolicy.from_dict(json.loads(json_str))
[docs] def copy(self) -> "RandomPolicy":
"""
:return: a copy of the DTO
"""
return self.from_dict(self.to_dict())