#1595: fix poorly merged tests + files

src/primaite/agents/hardcoded_abc.py

@@ -73,8 +73,8 @@ class HardCodedAgentSessionABC(AgentSessionABC):
         self._env.set_as_eval()  # noqa
         self.is_eval = True
 
-        time_steps = self._training_config.num_steps
-        episodes = self._training_config.num_episodes
+        time_steps = self._training_config.num_eval_steps
+        episodes = self._training_config.num_eval_episodes
 
         obs = self._env.reset()
         for episode in range(episodes):

tests/config/one_node_states_on_off_main_config.yaml

@@ -7,6 +7,14 @@
 # "CUSTOM" (Custom Agent)
 agent_framework: CUSTOM
 
+# Sets which deep learning framework will be used (by RLlib ONLY).
+# Default is TF (Tensorflow).
+# Options are:
+# "TF" (Tensorflow)
+# TF2 (Tensorflow 2.X)
+# TORCH (PyTorch)
+deep_learning_framework: TF2
+
 # Sets which Agent class will be used.
 # Options are:
 # "A2C" (Advantage Actor Critic coupled with either SB3 or RLLIB agent_framework)
@@ -17,32 +25,78 @@ agent_framework: CUSTOM
 # "DUMMY" (primaite.agents.simple.DummyAgent)
 agent_identifier: DUMMY
 
+# Sets whether Red Agent POL and IER is randomised.
+# Options are:
+# True
+# False
+random_red_agent: False
+
+# The (integer) seed to be used in random number generation
+# Default is None (null)
+seed: null
+
+# Set whether the agent will be deterministic instead of stochastic
+# Options are:
+# True
+# False
+deterministic: False
+
+# Sets what view of the environment the deterministic hardcoded agent has. The default is BASIC.
+# Options are:
+# "BASIC" (The current observation space only)
+# "FULL" (Full environment view with actions taken and reward feedback)
+hard_coded_agent_view: FULL
+
 # Sets How the Action Space is defined:
 # "NODE"
 # "ACL"
 # "ANY" node and acl actions
 action_type: NODE
+# observation space
+observation_space:
+  # flatten: true
+  components:
+    - name: NODE_LINK_TABLE
+    # - name: NODE_STATUSES
+    # - name: LINK_TRAFFIC_LEVELS
 
 
+# Number of episodes for training to run per session
+num_train_episodes: 10
+
+# Number of time_steps for training per episode
+num_train_steps: 256
+
 # Number of episodes for evaluation to run per session
 num_eval_episodes: 1
 
 # Number of time_steps for evaluation per episode
 num_eval_steps: 15
-# Time delay between steps (for generic agents)
-time_delay: 1
 
-# Type of session to be run (TRAINING or EVALUATION)
+# Sets how often the agent will save a checkpoint (every n time episodes).
+# Set to 0 if no checkpoints are required. Default is 10
+checkpoint_every_n_episodes: 10
+
+# Time delay (milliseconds) between steps for CUSTOM agents.
+time_delay: 5
+
+# Type of session to be run. Options are:
+# "TRAIN" (Trains an agent)
+# "EVAL" (Evaluates an agent)
+# "TRAIN_EVAL" (Trains then evaluates an agent)
 session_type: EVAL
-# Determine whether to load an agent from file
-load_agent: False
-# File path and file name of agent if you're loading one in
-agent_load_file: C:\[Path]\[agent_saved_filename.zip]
 
 # Environment config values
 # The high value for the observation space
 observation_space_high_value: 1000000000
 
+# The Stable Baselines3 learn/eval output verbosity level:
+# Options are:
+# "NONE" (No Output)
+# "INFO" (Info Messages (such as devices and wrappers used))
+# "DEBUG" (All Messages)
+sb3_output_verbose_level: NONE
+
 # Reward values
 # Generic
 all_ok: 0

tests/config/test_random_red_main_config.yaml

@@ -5,7 +5,15 @@
 # "SB3" (Stable Baselines3)
 # "RLLIB" (Ray RLlib)
 # "CUSTOM" (Custom Agent)
-agent_framework: CUSTOM
+agent_framework: SB3
+
+# Sets which deep learning framework will be used (by RLlib ONLY).
+# Default is TF (Tensorflow).
+# Options are:
+# "TF" (Tensorflow)
+# TF2 (Tensorflow 2.X)
+# TORCH (PyTorch)
+deep_learning_framework: TF2
 
 # Sets which Agent class will be used.
 # Options are:
@@ -15,7 +23,7 @@ agent_framework: CUSTOM
 # "DO_NOTHING" (The DoNothing agents coupled with an ACL or NODE action_type)
 # "RANDOM" (primaite.agents.simple.RandomAgent)
 # "DUMMY" (primaite.agents.simple.DummyAgent)
-agent_identifier: DUMMY
+agent_identifier: PPO
 
 # Sets whether Red Agent POL and IER is randomised.
 # Options are:
@@ -23,92 +31,128 @@ agent_identifier: DUMMY
 # False
 random_red_agent: True
 
+# The (integer) seed to be used in random number generation
+# Default is None (null)
+seed: null
+
+# Set whether the agent will be deterministic instead of stochastic
+# Options are:
+# True
+# False
+deterministic: False
+
+# Sets what view of the environment the deterministic hardcoded agent has. The default is BASIC.
+# Options are:
+# "BASIC" (The current observation space only)
+# "FULL" (Full environment view with actions taken and reward feedback)
+hard_coded_agent_view: FULL
+
 # Sets How the Action Space is defined:
 # "NODE"
 # "ACL"
 # "ANY" node and acl actions
 action_type: NODE
+# observation space
+observation_space:
+  # flatten: true
+  components:
+    - name: NODE_LINK_TABLE
+    # - name: NODE_STATUSES
+    # - name: LINK_TRAFFIC_LEVELS
+
+
 # Number of episodes for training to run per session
-num_train_episodes: 2
+num_train_episodes: 10
 
 # Number of time_steps for training per episode
-num_train_steps: 15
+num_train_steps: 256
 
 # Number of episodes for evaluation to run per session
-num_eval_episodes: 2
+num_eval_episodes: 1
 
 # Number of time_steps for evaluation per episode
-num_eval_steps: 15
-# Time delay between steps (for generic agents)
-time_delay: 1
+num_eval_steps: 256
 
-# Type of session to be run (TRAINING or EVALUATION)
-session_type: EVAL
-# Determine whether to load an agent from file
-load_agent: False
-# File path and file name of agent if you're loading one in
-agent_load_file: C:\[Path]\[agent_saved_filename.zip]
+# Sets how often the agent will save a checkpoint (every n time episodes).
+# Set to 0 if no checkpoints are required. Default is 10
+checkpoint_every_n_episodes: 10
+
+# Time delay (milliseconds) between steps for CUSTOM agents.
+time_delay: 5
+
+# Type of session to be run. Options are:
+# "TRAIN" (Trains an agent)
+# "EVAL" (Evaluates an agent)
+# "TRAIN_EVAL" (Trains then evaluates an agent)
+session_type: TRAIN_EVAL
 
 # Environment config values
 # The high value for the observation space
 observation_space_high_value: 1000000000
 
+# The Stable Baselines3 learn/eval output verbosity level:
+# Options are:
+# "NONE" (No Output)
+# "INFO" (Info Messages (such as devices and wrappers used))
+# "DEBUG" (All Messages)
+sb3_output_verbose_level: NONE
+
 # Reward values
 # Generic
 all_ok: 0
 # Node Hardware State
-off_should_be_on: -10
-off_should_be_resetting: -5
-on_should_be_off: -2
-on_should_be_resetting: -5
-resetting_should_be_on: -5
-resetting_should_be_off: -2
-resetting: -3
+off_should_be_on: -0.001
+off_should_be_resetting: -0.0005
+on_should_be_off: -0.0002
+on_should_be_resetting: -0.0005
+resetting_should_be_on: -0.0005
+resetting_should_be_off: -0.0002
+resetting: -0.0003
 # Node Software or Service State
-good_should_be_patching: 2
-good_should_be_compromised: 5
-good_should_be_overwhelmed: 5
-patching_should_be_good: -5
-patching_should_be_compromised: 2
-patching_should_be_overwhelmed: 2
-patching: -3
-compromised_should_be_good: -20
-compromised_should_be_patching: -20
-compromised_should_be_overwhelmed: -20
-compromised: -20
-overwhelmed_should_be_good: -20
-overwhelmed_should_be_patching: -20
-overwhelmed_should_be_compromised: -20
-overwhelmed: -20
+good_should_be_patching: 0.0002
+good_should_be_compromised: 0.0005
+good_should_be_overwhelmed: 0.0005
+patching_should_be_good: -0.0005
+patching_should_be_compromised: 0.0002
+patching_should_be_overwhelmed: 0.0002
+patching: -0.0003
+compromised_should_be_good: -0.002
+compromised_should_be_patching: -0.002
+compromised_should_be_overwhelmed: -0.002
+compromised: -0.002
+overwhelmed_should_be_good: -0.002
+overwhelmed_should_be_patching: -0.002
+overwhelmed_should_be_compromised: -0.002
+overwhelmed: -0.002
 # Node File System State
-good_should_be_repairing: 2
-good_should_be_restoring: 2
-good_should_be_corrupt: 5
-good_should_be_destroyed: 10
-repairing_should_be_good: -5
-repairing_should_be_restoring: 2
-repairing_should_be_corrupt: 2
-repairing_should_be_destroyed: 0
-repairing: -3
-restoring_should_be_good: -10
-restoring_should_be_repairing: -2
-restoring_should_be_corrupt: 1
-restoring_should_be_destroyed: 2
-restoring: -6
-corrupt_should_be_good: -10
-corrupt_should_be_repairing: -10
-corrupt_should_be_restoring: -10
-corrupt_should_be_destroyed: 2
-corrupt: -10
-destroyed_should_be_good: -20
-destroyed_should_be_repairing: -20
-destroyed_should_be_restoring: -20
-destroyed_should_be_corrupt: -20
-destroyed: -20
-scanning: -2
+good_should_be_repairing: 0.0002
+good_should_be_restoring: 0.0002
+good_should_be_corrupt: 0.0005
+good_should_be_destroyed: 0.001
+repairing_should_be_good: -0.0005
+repairing_should_be_restoring: 0.0002
+repairing_should_be_corrupt: 0.0002
+repairing_should_be_destroyed: 0.0000
+repairing: -0.0003
+restoring_should_be_good: -0.001
+restoring_should_be_repairing: -0.0002
+restoring_should_be_corrupt: 0.0001
+restoring_should_be_destroyed: 0.0002
+restoring: -0.0006
+corrupt_should_be_good: -0.001
+corrupt_should_be_repairing: -0.001
+corrupt_should_be_restoring: -0.001
+corrupt_should_be_destroyed: 0.0002
+corrupt: -0.001
+destroyed_should_be_good: -0.002
+destroyed_should_be_repairing: -0.002
+destroyed_should_be_restoring: -0.002
+destroyed_should_be_corrupt: -0.002
+destroyed: -0.002
+scanning: -0.0002
 # IER status
-red_ier_running: -5
-green_ier_blocked: -10
+red_ier_running: -0.0005
+green_ier_blocked: -0.001
 
 # Patching / Reset durations
 os_patching_duration: 5            # The time taken to patch the OS

tests/conftest.py

@@ -62,7 +62,6 @@ class TempPrimaiteSession(PrimaiteSession):
 
     def __exit__(self, type, value, tb):
         shutil.rmtree(self.session_path)
-        shutil.rmtree(self.session_path.parent)
         _LOGGER.debug(f"Deleted temp session directory: {self.session_path}")
 
 
@@ -114,7 +113,7 @@ def temp_primaite_session(request):
     """
     training_config_path = request.param[0]
     lay_down_config_path = request.param[1]
-    with patch("primaite.agents.agent.get_session_path", get_temp_session_path) as mck:
+    with patch("primaite.agents.agent_abc.get_session_path", get_temp_session_path) as mck:
         mck.session_timestamp = datetime.now()
 
         return TempPrimaiteSession(training_config_path, lay_down_config_path)