belief aggregation, stopping

Author: Limmen

examples/manual_play/intrusion_recovery_pomdp.py

@@ -34,7 +34,7 @@
         actions=IntrusionRecoveryPomdpUtil.action_space(),
         observations=IntrusionRecoveryPomdpUtil.observation_space(num_observations=num_observations),
         cost_tensor=cost_tensor, observation_tensor=observation_tensor, transition_tensor=transition_tensor,
-        b1=IntrusionRecoveryPomdpUtil.initial_belief(p_a=p_a), T=BTR,
+        b1=IntrusionRecoveryPomdpUtil.initial_belief(), T=BTR,
         simulation_env_name=simulation_name, gym_env_name="csle-tolerance-intrusion-recovery-pomdp-v1"
     )
     env = gym.make("csle-tolerance-intrusion-recovery-pomdp-v1",

examples/training/bayesian_optimization/intrusion_recovery_pomdp_defender/run_v_001.py

@@ -46,7 +46,7 @@
         actions=IntrusionRecoveryPomdpUtil.action_space(),
         observations=IntrusionRecoveryPomdpUtil.observation_space(num_observations=num_observations),
         cost_tensor=cost_tensor, observation_tensor=observation_tensor, transition_tensor=transition_tensor,
-        b1=IntrusionRecoveryPomdpUtil.initial_belief(p_a=p_a), T=BTR,
+        b1=IntrusionRecoveryPomdpUtil.initial_belief(), T=BTR,
         simulation_env_name=simulation_name, gym_env_name="csle-tolerance-intrusion-recovery-pomdp-v1"
     )
     simulation_env_config.simulation_env_input_config = input_config

examples/training/cross_entropy/intrusion_recovery_pomdp_defender/run_v_001.py

@@ -46,7 +46,7 @@
         actions=IntrusionRecoveryPomdpUtil.action_space(),
         observations=IntrusionRecoveryPomdpUtil.observation_space(num_observations=num_observations),
         cost_tensor=cost_tensor, observation_tensor=observation_tensor, transition_tensor=transition_tensor,
-        b1=IntrusionRecoveryPomdpUtil.initial_belief(p_a=p_a), T=BTR,
+        b1=IntrusionRecoveryPomdpUtil.initial_belief(), T=BTR,
         simulation_env_name=simulation_name, gym_env_name="csle-tolerance-intrusion-recovery-pomdp-v1"
     )
     simulation_env_config.simulation_env_input_config = input_config

examples/training/differential_evolution/intrusion_recovery_pomdp_defender/run_v_001.py

@@ -46,7 +46,7 @@
         actions=IntrusionRecoveryPomdpUtil.action_space(),
         observations=IntrusionRecoveryPomdpUtil.observation_space(num_observations=num_observations),
         cost_tensor=cost_tensor, observation_tensor=observation_tensor, transition_tensor=transition_tensor,
-        b1=IntrusionRecoveryPomdpUtil.initial_belief(p_a=p_a), T=BTR,
+        b1=IntrusionRecoveryPomdpUtil.initial_belief(), T=BTR,
         simulation_env_name=simulation_name, gym_env_name="csle-tolerance-intrusion-recovery-pomdp-v1"
     )
     simulation_env_config.simulation_env_input_config = input_config

examples/training/pomdp_solve/intrusion_recovery_pomdp/run_vs_random_attacker_v_001.py

@@ -29,7 +29,7 @@
         actions=IntrusionRecoveryPomdpUtil.action_space(),
         observations=IntrusionRecoveryPomdpUtil.observation_space(num_observations=num_observations),
         cost_tensor=cost_tensor, observation_tensor=observation_tensor, transition_tensor=transition_tensor,
-        b1=IntrusionRecoveryPomdpUtil.initial_belief(p_a=p_a), T=BTR,
+        b1=IntrusionRecoveryPomdpUtil.initial_belief(), T=BTR,
         simulation_env_name=simulation_name, gym_env_name="csle-tolerance-intrusion-recovery-pomdp-v1"
     )
     pomdp_solve_file_str = IntrusionRecoveryPomdpUtil.pomdp_solver_file(config=config)

examples/training/posg_solve/intrusion_recovery_pomdp/run_vs_random_attacker_v_001.py

@@ -26,7 +26,7 @@
         actions=IntrusionRecoveryPomdpUtil.action_space(),
         observations=IntrusionRecoveryPomdpUtil.observation_space(num_observations=num_observations),
         cost_tensor=cost_tensor, observation_tensor=observation_tensor, transition_tensor=transition_tensor,
-        b1=IntrusionRecoveryPomdpUtil.initial_belief(p_a=p_a), T=BTR,
+        b1=IntrusionRecoveryPomdpUtil.initial_belief(), T=BTR,
         simulation_env_name=simulation_name, gym_env_name="csle-tolerance-intrusion-recovery-pomdp-v1"
     )
 

examples/training/ppo/intrusion_recovery_pomdp_defender/run_v_001.py

@@ -43,7 +43,7 @@
         actions=IntrusionRecoveryPomdpUtil.action_space(),
         observations=IntrusionRecoveryPomdpUtil.observation_space(num_observations=num_observations),
         cost_tensor=cost_tensor, observation_tensor=observation_tensor, transition_tensor=transition_tensor,
-        b1=IntrusionRecoveryPomdpUtil.initial_belief(p_a=p_a), T=BTR,
+        b1=IntrusionRecoveryPomdpUtil.initial_belief(), T=BTR,
         simulation_env_name=simulation_name, gym_env_name="csle-tolerance-intrusion-recovery-pomdp-v1"
     )
     input_config.max_horizon = 30

examples/training/random_search/intrusion_recovery_pomdp_defender/run_v_001.py

@@ -45,7 +45,7 @@
         actions=IntrusionRecoveryPomdpUtil.action_space(),
         observations=IntrusionRecoveryPomdpUtil.observation_space(num_observations=num_observations),
         cost_tensor=cost_tensor, observation_tensor=observation_tensor, transition_tensor=transition_tensor,
-        b1=IntrusionRecoveryPomdpUtil.initial_belief(p_a=p_a), T=BTR,
+        b1=IntrusionRecoveryPomdpUtil.initial_belief(), T=BTR,
         simulation_env_name=simulation_name, gym_env_name="csle-tolerance-intrusion-recovery-pomdp-v1"
     )
     simulation_env_config.simulation_env_input_config = input_config

examples/training/t_spsa/intrusion_recovery_pomdp_defender/run_v_001.py

@@ -44,7 +44,7 @@
         actions=IntrusionRecoveryPomdpUtil.action_space(),
         observations=IntrusionRecoveryPomdpUtil.observation_space(num_observations=num_observations),
         cost_tensor=cost_tensor, observation_tensor=observation_tensor, transition_tensor=transition_tensor,
-        b1=IntrusionRecoveryPomdpUtil.initial_belief(p_a=p_a), T=BTR,
+        b1=IntrusionRecoveryPomdpUtil.initial_belief(), T=BTR,
         simulation_env_name=simulation_name, gym_env_name="csle-tolerance-intrusion-recovery-pomdp-v1"
     )
     simulation_env_config.simulation_env_input_config = input_config

simulation-system/envs/intrusion_recovery_pomdp_defender/config_v_001.py

@@ -262,8 +262,7 @@ def default_initial_state_distribution_config(p_a: float) -> InitialStateDistrib
     :return: the default initial state distribution configuration
     """
     initial_state_distribution_config = InitialStateDistributionConfig(
-        initial_state_distribution=IntrusionRecoveryPomdpUtil.initial_belief(p_a=p_a)
-    )
+        initial_state_distribution=IntrusionRecoveryPomdpUtil.initial_belief())
     return initial_state_distribution_config
 
 
@@ -301,7 +300,7 @@ def default_input_config(eta: float, p_a: float, p_c_1: float, p_c_2: float, p_u
         actions=IntrusionRecoveryPomdpUtil.action_space(),
         observations=IntrusionRecoveryPomdpUtil.observation_space(num_observations=num_observations),
         cost_tensor=cost_tensor, observation_tensor=observation_tensor, transition_tensor=transition_tensor,
-        b1=IntrusionRecoveryPomdpUtil.initial_belief(p_a=p_a), T=BTR,
+        b1=IntrusionRecoveryPomdpUtil.initial_belief(), T=BTR,
         simulation_env_name=simulation_env_name, gym_env_name="csle-tolerance-intrusion-recovery-pomdp-v1"
     )
     return config

simulation-system/libs/csle-tolerance/src/csle_tolerance/util/intrusion_recovery_pomdp_util.py

@@ -24,7 +24,7 @@ def state_space() -> List[int]:
         return [0, 1, 2]
 
     @staticmethod
-    def initial_belief(p_a: float) -> List[float]:
+    def initial_belief() -> List[float]:
         """
         Gets the initial belief state of the POMDP
 
@@ -72,9 +72,7 @@ def cost_function(s: int, a: int, eta: float, negate: bool = False) -> float:
             return cost
 
     @staticmethod
-    def cost_tensor(
-        eta: float, states: List[int], actions: List[int], negate: bool = False
-    ) -> List[List[float]]:
+    def cost_tensor(eta: float, states: List[int], actions: List[int], negate: bool = False) -> List[List[float]]:
         """
         Creates a |A|x|S| tensor with the costs (or rewards) of the POMDP
 
@@ -119,9 +117,7 @@ def observation_function(s: int, o: int, num_observations: int) -> float:
                 return 0.0
 
     @staticmethod
-    def observation_tensor(
-        states: List[int], observations: List[int]
-    ) -> List[List[float]]:
+    def observation_tensor(states: List[int], observations: List[int]) -> List[List[float]]:
         """
         Creates a |S|x|O| tensor with the observation probabilities
 
@@ -143,9 +139,7 @@ def observation_tensor(
         return observation_tensor
 
     @staticmethod
-    def transition_function(
-        s: int, s_prime: int, a: int, p_a: float, p_c_1: float, p_u: float, p_c_2: float
-    ) -> float:
+    def transition_function(s: int, s_prime: int, a: int, p_a: float, p_c_1: float, p_u: float, p_c_2: float) -> float:
         """
         The transition function of the POMDP
 
@@ -184,9 +178,7 @@ def transition_function(
             return 0
 
     @staticmethod
-    def transition_function_game(
-        s: int, s_prime: int, a1: int, a2: int, p_a: float, p_c_1: float
-    ) -> float:
+    def transition_function_game(s: int, s_prime: int, a1: int, a2: int, p_a: float, p_c_1: float) -> float:
         """
         The transition function of the POSG
 
@@ -216,14 +208,8 @@ def transition_function_game(
             return 0
 
     @staticmethod
-    def transition_tensor(
-        states: List[int],
-        actions: List[int],
-        p_a: float,
-        p_c_1: float,
-        p_c_2: float,
-        p_u: float,
-    ) -> List[List[List[float]]]:
+    def transition_tensor(states: List[int], actions: List[int], p_a: float, p_c_1: float, p_c_2: float, p_u: float) \
+            -> List[List[List[float]]]:
         """
         Creates a |A|x|S|x|S| tensor with the transition probabilities of the POMDP
 
@@ -258,13 +244,8 @@ def transition_tensor(
         return transition_tensor
 
     @staticmethod
-    def transition_tensor_game(
-        states: List[int],
-        defender_actions: List[int],
-        attacker_actions: List[int],
-        p_a: float,
-        p_c_1: float,
-    ) -> List[List[List[List[float]]]]:
+    def transition_tensor_game(states: List[int], defender_actions: List[int], attacker_actions: List[int], p_a: float,
+                               p_c_1: float) -> List[List[List[List[float]]]]:
         """
         Creates a |A|x|A|x|S|x|S| tensor with the transition probabilities of the POSG
 
@@ -304,9 +285,7 @@ def sample_initial_state(b1: List[float]) -> int:
         return int(np.random.choice(np.arange(0, len(b1)), p=b1))
 
     @staticmethod
-    def sample_next_observation(
-        observation_tensor: List[List[float]], s_prime: int, observations: List[int]
-    ) -> int:
+    def sample_next_observation(observation_tensor: List[List[float]], s_prime: int, observations: List[int]) -> int:
         """
         Samples the next observation
 
@@ -322,9 +301,7 @@ def sample_next_observation(
         return int(o)
 
     @staticmethod
-    def sample_next_state_game(
-        transition_tensor: List[List[List[List[float]]]], s: int, a1: int, a2: int
-    ) -> int:
+    def sample_next_state_game(transition_tensor: List[List[List[List[float]]]], s: int, a1: int, a2: int) -> int:
         """
         Samples the next observation
 
@@ -341,16 +318,8 @@ def sample_next_state_game(
         return int(s_prime)
 
     @staticmethod
-    def bayes_filter(
-        s_prime: int,
-        o: int,
-        a: int,
-        b: List[float],
-        states: List[int],
-        observations: List[int],
-        observation_tensor: List[List[float]],
-        transition_tensor: List[List[List[float]]],
-    ) -> float:
+    def bayes_filter(s_prime: int, o: int, a: int, b: List[float], states: List[int], observations: List[int],
+                     observation_tensor: List[List[float]], transition_tensor: List[List[List[float]]]) -> float:
         """
         A Bayesian filter to compute b[s_prime] of the POMDP
 
@@ -386,14 +355,8 @@ def bayes_filter(
         return b_prime_s_prime
 
     @staticmethod
-    def p_o_given_b_a1_a2(
-        o: int,
-        b: List[float],
-        a: int,
-        states: List[int],
-        transition_tensor: List[List[List[float]]],
-        observation_tensor: List[List[float]],
-    ) -> float:
+    def p_o_given_b_a1_a2(o: int, b: List[float], a: int, states: List[int], transition_tensor: List[List[List[float]]],
+                          observation_tensor: List[List[float]]) -> float:
         """
         Computes P[o|a,b] of the POMDP
 
@@ -417,15 +380,8 @@ def p_o_given_b_a1_a2(
         return prob
 
     @staticmethod
-    def next_belief(
-        o: int,
-        a: int,
-        b: List[float],
-        states: List[int],
-        observations: List[int],
-        observation_tensor: List[List[float]],
-        transition_tensor: List[List[List[float]]],
-    ) -> List[float]:
+    def next_belief(o: int, a: int, b: List[float], states: List[int], observations: List[int],
+                    observation_tensor: List[List[float]], transition_tensor: List[List[List[float]]]) -> List[float]:
         """
         Computes the next belief using a Bayesian filter
 
@@ -441,15 +397,8 @@ def next_belief(
         b_prime = [0.0] * len(states)
         for s_prime in states:
             b_prime[s_prime] = IntrusionRecoveryPomdpUtil.bayes_filter(
-                s_prime=s_prime,
-                o=o,
-                a=a,
-                b=b,
-                states=states,
-                observations=observations,
-                transition_tensor=transition_tensor,
-                observation_tensor=observation_tensor,
-            )
+                s_prime=s_prime, o=o, a=a, b=b, states=states, observations=observations,
+                transition_tensor=transition_tensor, observation_tensor=observation_tensor)
         if round(sum(b_prime), 2) != 1:
             print(f"error, b_prime:{b_prime}, o:{o}, a:{a}, b:{b}")
         assert round(sum(b_prime), 2) == 1

simulation-system/libs/csle-tolerance/tests/test_intrusion_recovery_game_config.py

@@ -223,7 +223,7 @@ def test_from_json_file(self, mocker: pytest_mock.MockFixture) -> None:
             cost_tensor=cost_tensor,
             observation_tensor=observation_tensor,
             transition_tensor=transition_tensor,
-            b1=IntrusionRecoveryPomdpUtil.initial_belief(p_a=p_a),
+            b1=IntrusionRecoveryPomdpUtil.initial_belief(),
             T=int(BTR),
             simulation_env_name=simulation_name,
             gym_env_name="csle-tolerance-intrusion-recovery-pomdp-v1",

simulation-system/libs/csle-tolerance/tests/test_intrusion_recovery_pomdp_config.py

@@ -244,7 +244,7 @@ def test_from_json_file(self, mocker: pytest_mock.MockFixture) -> None:
             cost_tensor=cost_tensor,
             observation_tensor=observation_tensor,
             transition_tensor=transition_tensor,
-            b1=IntrusionRecoveryPomdpUtil.initial_belief(p_a=p_a),
+            b1=IntrusionRecoveryPomdpUtil.initial_belief(),
             T=BTR,
             simulation_env_name=simulation_name,
             gym_env_name="csle-tolerance-intrusion-recovery-pomdp-v1",

simulation-system/libs/csle-tolerance/tests/test_intrusion_recovery_pomdp_util.py

@@ -30,7 +30,7 @@ def test_initial_belief(self) -> None:
 
         :return: None
         """
-        assert sum(IntrusionRecoveryPomdpUtil.initial_belief(p_a=0.5)) == 1
+        assert sum(IntrusionRecoveryPomdpUtil.initial_belief()) == 1
 
     def test_action_space(self) -> None:
         """