Merged PR 228: Fix annoying Ray issues

## Summary
Fix ray episode issues. Allow ray to manage its environment fully - this fixes the perma-zero reward.

## Test process
Running notebooks and primaite sessions.

## Checklist
- [ ] PR is linked to a **work item**
- [ ] **acceptance criteria** of linked ticket are met
- [ ] performed **self-review** of the code
- [ ] written **tests** for any new functionality added with this PR
- [ ] updated the **documentation** if this PR changes or adds functionality
- [ ] written/updated **design docs** if this PR implements new functionality
- [ ] updated the **change log**
- [ ] ran **pre-commit** checks for code style
- [ ] attended to any **TO-DOs** left in the code

Related work items: #2095

.gitignore

@@ -155,3 +155,4 @@ simulation_output/
 benchmark/output
 # src/primaite/notebooks/scratch.ipynb
 src/primaite/notebooks/scratch.py
+sandbox.py

src/primaite/config/_package_data/example_config.yaml

@@ -655,8 +655,8 @@ simulation:
       - ref: data_manipulation_bot
         type: DataManipulationBot
         options:
-          port_scan_p_of_success: 0.1
+          port_scan_p_of_success: 0.8
-          data_manipulation_p_of_success: 0.1
+          data_manipulation_p_of_success: 0.8
           payload: "DELETE"
           server_ip: 192.168.1.14
       services:

src/primaite/config/_package_data/example_config_2_rl_agents.yaml

@@ -1,14 +1,10 @@
 training_config:
-  rl_framework: RLLIB_single_agent
+  rl_framework: RLLIB_multi_agent
-  rl_algorithm: PPO
+  # rl_framework: SB3
-  seed: 333
+  n_agents: 2
-  n_learn_episodes: 1
-  n_eval_episodes: 5
-  max_steps_per_episode: 256
-  deterministic_eval: false
-  n_agents: 1
   agent_references:
-    - defender
+    - defender_1
+    - defender_2
 
 io_settings:
   save_checkpoints: true
@@ -36,28 +32,23 @@ agents:
     action_space:
       action_list:
         - type: DONOTHING
-        # <not yet implemented>
+        - type: NODE_APPLICATION_EXECUTE
-        # - type: NODE_LOGON
-        # - type: NODE_LOGOFF
-        # - type: NODE_APPLICATION_EXECUTE
-          # options:
-            # execution_definition:
-              # target_address: arcd.com
-
       options:
         nodes:
         - node_ref: client_2
+          applications:
+            - application_ref: client_2_web_browser
         max_folders_per_node: 1
         max_files_per_folder: 1
         max_services_per_node: 1
-        max_nics_per_node: 2
+        max_applications_per_node: 1
-        max_acl_rules: 10
 
     reward_function:
       reward_components:
         - type: DUMMY
 
     agent_settings:
+      start_settings:
         start_step: 5
         frequency: 4
         variance: 3
@@ -69,38 +60,20 @@ agents:
     observation_space:
       type: UC2RedObservation
       options:
-        nodes:
+        nodes: {}
-          - node_ref: client_1
-            observations:
-            - logon_status
-            - operating_status
-            services:
-            - service_ref: data_manipulation_bot
-              observations:
-                operating_status
-                health_status
-            folders: {}
 
     action_space:
       action_list:
         - type: DONOTHING
-        #<not yet implemented
+        - type: NODE_APPLICATION_EXECUTE
-        # - type: NODE_APPLICATION_EXECUTE
-        #   options:
-        #     execution_definition:
-        #       server_ip: 192.168.1.14
-        #       payload: "DELETE"
-        #       success_rate: 80%
         - type: NODE_FILE_DELETE
         - type: NODE_FILE_CORRUPT
-        # - type: NODE_FOLDER_DELETE
-        # - type: NODE_FOLDER_CORRUPT
         - type: NODE_OS_SCAN
-        # - type: NODE_LOGON
-        # - type: NODE_LOGOFF
       options:
         nodes:
         - node_ref: client_1
+          applications:
+            - application_ref: data_manipulation_bot
         max_folders_per_node: 1
         max_files_per_folder: 1
         max_services_per_node: 1
@@ -110,11 +83,12 @@ agents:
         - type: DUMMY
 
     agent_settings: # options specific to this particular agent type, basically args of __init__(self)
+      start_settings:
         start_step: 25
         frequency: 20
         variance: 5
 
-  - ref: defender1
+  - ref: defender_1
     team: BLUE
     type: ProxyAgent
 
@@ -544,7 +518,9 @@ agents:
 
     agent_settings:
       # ...
-  - ref: defender2
+
+
+  - ref: defender_2
     team: BLUE
     type: ProxyAgent
 
@@ -992,17 +968,25 @@ simulation:
           ip_address: 192.168.1.1
           subnet_mask: 255.255.255.0
         2:
-          ip_address: 192.168.1.1
+          ip_address: 192.168.10.1
           subnet_mask: 255.255.255.0
       acl:
-        0:
+        18:
           action: PERMIT
           src_port: POSTGRES_SERVER
           dst_port: POSTGRES_SERVER
-        1:
+        19:
           action: PERMIT
           src_port: DNS
           dst_port: DNS
+        20:
+          action: PERMIT
+          src_port: FTP
+          dst_port: FTP
+        21:
+          action: PERMIT
+          src_port: HTTP
+          dst_port: HTTP
         22:
           action: PERMIT
           src_port: ARP
@@ -1039,7 +1023,7 @@ simulation:
       hostname: web_server
       ip_address: 192.168.1.12
       subnet_mask: 255.255.255.0
-      default_gateway: 192.168.1.10
+      default_gateway: 192.168.1.1
       dns_server: 192.168.1.10
       services:
       - ref: web_server_database_client
@@ -1060,6 +1044,10 @@ simulation:
       services:
       - ref: database_service
         type: DatabaseService
+        options:
+          backup_server_ip: 192.168.1.16
+      - ref: database_ftp_client
+        type: FTPClient
 
     - ref: backup_server
       type: server
@@ -1070,7 +1058,7 @@ simulation:
       dns_server: 192.168.1.10
       services:
       - ref: backup_service
-        type: DatabaseBackup
+        type: FTPServer
 
     - ref: security_suite
       type: server
@@ -1091,9 +1079,15 @@ simulation:
       subnet_mask: 255.255.255.0
       default_gateway: 192.168.10.1
       dns_server: 192.168.1.10
-      services:
+      applications:
       - ref: data_manipulation_bot
         type: DataManipulationBot
+        options:
+          port_scan_p_of_success: 0.1
+          data_manipulation_p_of_success: 0.1
+          payload: "DELETE"
+          server_ip: 192.168.1.14
+      services:
       - ref: client_1_dns_client
         type: DNSClient
 
@@ -1107,10 +1101,14 @@ simulation:
       applications:
       - ref: client_2_web_browser
         type: WebBrowser
+        options:
+          target_url: http://arcd.com/users/
       services:
       - ref: client_2_dns_client
         type: DNSClient
 
+
+
     links:
     - ref: router_1___switch_1
       endpoint_a_ref: router_1

src/primaite/game/agent/rewards.py

@@ -238,7 +238,8 @@ class RewardFunction:
         """Initialise the reward function object."""
         self.reward_components: List[Tuple[AbstractReward, float]] = []
         "attribute reward_components keeps track of reward components and the weights assigned to each."
-        self.current_reward: float
+        self.current_reward: float = 0.0
+        self.total_reward: float = 0.0
 
     def regsiter_component(self, component: AbstractReward, weight: float = 1.0) -> None:
         """Add a reward component to the reward function.

src/primaite/game/game.py

@@ -125,6 +125,7 @@ class PrimaiteGame:
         for agent in self.agents:
             agent.update_observation(state)
             agent.update_reward(state)
+            agent.reward_function.total_reward += agent.reward_function.current_reward
 
     def apply_agent_actions(self) -> None:
         """Apply all actions to simulation as requests."""
@@ -155,6 +156,8 @@ class PrimaiteGame:
         self.step_counter = 0
         _LOGGER.debug(f"Resetting primaite game, episode = {self.episode_counter}")
         self.simulation.reset_component_for_episode(episode=self.episode_counter)
+        for agent in self.agents:
+            agent.reward_function.total_reward = 0.0
 
     def close(self) -> None:
         """Close the game, this will close the simulation."""
@@ -240,7 +243,7 @@ class PrimaiteGame:
                             position=r_num,
                         )
             else:
-                print("invalid node type")
+                _LOGGER.warning(f"invalid node type {n_type} in config")
             if "services" in node_cfg:
                 for service_cfg in node_cfg["services"]:
                     new_service = None
@@ -256,12 +259,12 @@ class PrimaiteGame:
                         "FTPServer": FTPServer,
                     }
                     if service_type in service_types_mapping:
-                        print(f"installing {service_type} on node {new_node.hostname}")
+                        _LOGGER.debug(f"installing {service_type} on node {new_node.hostname}")
                         new_node.software_manager.install(service_types_mapping[service_type])
                         new_service = new_node.software_manager.software[service_type]
                         game.ref_map_services[service_ref] = new_service.uuid
                     else:
-                        print(f"service type not found {service_type}")
+                        _LOGGER.warning(f"service type not found {service_type}")
                     # service-dependent options
                     if service_type == "DatabaseClient":
                         if "options" in service_cfg:
@@ -295,7 +298,7 @@ class PrimaiteGame:
                         new_application = new_node.software_manager.software[application_type]
                         game.ref_map_applications[application_ref] = new_application.uuid
                     else:
-                        print(f"application type not found {application_type}")
+                        _LOGGER.warning(f"application type not found {application_type}")
 
                     if application_type == "DataManipulationBot":
                         if "options" in application_cfg:
@@ -416,7 +419,7 @@ class PrimaiteGame:
                 )
                 game.agents.append(new_agent)
             else:
-                print("agent type not found")
+                _LOGGER.warning(f"agent type {agent_type} not found")
 
         game.simulation.set_original_state()
 

src/primaite/notebooks/training_example_ray_multi_agent.ipynb

@@ -1,5 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train a Multi agent system using RLLIB\n",
+    "\n",
+    "This notebook will demonstrate how to use the `PrimaiteRayMARLEnv` to train a very basic system with two PPO agents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### First, Import packages and read our config file."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -8,75 +24,56 @@
    "source": [
     "from primaite.game.game import PrimaiteGame\n",
     "import yaml\n",
-    "from primaite.config.load import example_config_path\n",
     "\n",
-    "from primaite.session.environment import PrimaiteRayEnv"
+    "from primaite.session.environment import PrimaiteRayEnv\n",
-   ]
+    "from primaite import PRIMAITE_PATHS\n",
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(example_config_path(), 'r') as f:\n",
-    "    cfg = yaml.safe_load(f)\n",
     "\n",
-    "game = PrimaiteGame.from_config(cfg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# gym = PrimaiteRayEnv({\"game\":game})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "import ray\n",
     "from ray import air, tune\n",
-    "from ray.rllib.algorithms.ppo import PPOConfig"
+    "from ray.rllib.algorithms.ppo import PPOConfig\n",
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ray.shutdown()\n",
-    "ray.init()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "from primaite.session.environment import PrimaiteRayMARLEnv\n",
     "\n",
+    "# If you get an error saying this config file doesn't exist, you may need to run `primaite setup` in your command line\n",
+    "# to copy the files to your user data path.\n",
+    "with open(PRIMAITE_PATHS.user_config_path / 'example_config/example_config_2_rl_agents.yaml', 'r') as f:\n",
+    "    cfg = yaml.safe_load(f)\n",
     "\n",
-    "env_config = {\"game\":game}\n",
+    "ray.init(local_mode=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create a Ray algorithm config which accepts our two agents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "config = (\n",
     "    PPOConfig()\n",
-    "    .environment(env=PrimaiteRayMARLEnv, env_config={\"game\":game})\n",
-    "    .rollouts(num_rollout_workers=0)\n",
     "    .multi_agent(\n",
-    "        policies={agent.agent_name for agent in game.rl_agents},\n",
+    "        policies={'defender_1','defender_2'}, # These names are the same as the agents defined in the example config.\n",
     "        policy_mapping_fn=lambda agent_id, episode, worker, **kw: agent_id,\n",
     "        )\n",
+    "    .environment(env=PrimaiteRayMARLEnv, env_config={\"cfg\":cfg})#, disable_env_checking=True)\n",
+    "    .rollouts(num_rollout_workers=0)\n",
     "    .training(train_batch_size=128)\n",
     "    )\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Set training parameters and start the training\n",
+    "This example will save outputs to a default Ray directory and use mostly default settings."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -86,21 +83,11 @@
     "tune.Tuner(\n",
     "    \"PPO\",\n",
     "    run_config=air.RunConfig(\n",
-    "        stop={\"training_iteration\": 128},\n",
+    "        stop={\"timesteps_total\": 512},\n",
-    "        checkpoint_config=air.CheckpointConfig(\n",
-    "            checkpoint_frequency=10,\n",
-    "        ),\n",
     "    ),\n",
     "    param_space=config\n",
     ").fit()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

src/primaite/notebooks/training_example_ray_single_agent.ipynb

@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train a Single agent system using RLLib\n",
+    "This notebook will demonstrate how to use PrimaiteRayEnv to train a basic PPO agent."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -10,19 +18,25 @@
     "import yaml\n",
     "from primaite.config.load import example_config_path\n",
     "\n",
-    "from primaite.session.environment import PrimaiteRayEnv"
+    "from primaite.session.environment import PrimaiteRayEnv\n",
-   ]
+    "from ray.rllib.algorithms import ppo\n",
-  },
+    "from ray import air, tune\n",
-  {
+    "import ray\n",
-   "cell_type": "code",
+    "from ray.rllib.algorithms.ppo import PPOConfig\n",
-   "execution_count": null,
+    "\n",
-   "metadata": {},
+    "# If you get an error saying this config file doesn't exist, you may need to run `primaite setup` in your command line\n",
-   "outputs": [],
+    "# to copy the files to your user data path.\n",
-   "source": [
     "with open(example_config_path(), 'r') as f:\n",
     "    cfg = yaml.safe_load(f)\n",
     "\n",
-    "game = PrimaiteGame.from_config(cfg)"
+    "ray.init(local_mode=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create a Ray algorithm and pass it our config."
    ]
   },
   {
@@ -31,7 +45,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "gym = PrimaiteRayEnv({\"game\":game})"
+    "env_config = {\"cfg\":cfg}\n",
+    "\n",
+    "config = (\n",
+    "    PPOConfig()\n",
+    "    .environment(env=PrimaiteRayEnv, env_config=env_config, disable_env_checking=True)\n",
+    "    .rollouts(num_rollout_workers=0)\n",
+    "    .training(train_batch_size=128)\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Set training parameters and start the training"
    ]
   },
   {
@@ -40,61 +68,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import ray\n",
+    "tune.Tuner(\n",
-    "from ray.rllib.algorithms import ppo"
+    "    \"PPO\",\n",
-   ]
+    "    run_config=air.RunConfig(\n",
-  },
+    "        stop={\"timesteps_total\": 512}\n",
-  {
+    "    ),\n",
-   "cell_type": "code",
+    "    param_space=config\n",
-   "execution_count": null,
+    ").fit()\n"
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ray.shutdown()\n",
-    "ray.init()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "env_config = {\"game\":game}\n",
-    "config = {\n",
-    "    \"env\" : PrimaiteRayEnv,\n",
-    "    \"env_config\" : env_config,\n",
-    "    \"disable_env_checking\": True,\n",
-    "    \"num_rollout_workers\": 0,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "algo = ppo.PPO(config=config)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(5):\n",
-    "    result = algo.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "algo.save(\"temp/deleteme\")"
    ]
   }
  ],

src/primaite/session/environment.py

@@ -37,11 +37,14 @@ class PrimaiteGymEnv(gymnasium.Env):
         terminated = False
         truncated = self.game.calculate_truncated()
         info = {}
-        print(f"Episode: {self.game.episode_counter}, Step: {self.game.step_counter}, Reward: {reward}")
         return next_obs, reward, terminated, truncated, info
 
     def reset(self, seed: Optional[int] = None) -> Tuple[ObsType, Dict[str, Any]]:
         """Reset the environment."""
+        print(
+            f"Resetting environment, episode {self.game.episode_counter}, "
+            f"avg. reward: {self.game.rl_agents[0].reward_function.total_reward}"
+        )
         self.game.reset()
         state = self.game.get_sim_state()
         self.game.update_agents(state)
@@ -69,14 +72,15 @@ class PrimaiteGymEnv(gymnasium.Env):
 class PrimaiteRayEnv(gymnasium.Env):
     """Ray wrapper that accepts a single `env_config` parameter in init function for compatibility with Ray."""
 
-    def __init__(self, env_config: Dict[str, PrimaiteGame]) -> None:
+    def __init__(self, env_config: Dict) -> None:
         """Initialise the environment.
 
         :param env_config: A dictionary containing the environment configuration. It must contain a single key, `game`
             which is the PrimaiteGame instance.
         :type env_config: Dict[str, PrimaiteGame]
         """
-        self.env = PrimaiteGymEnv(game=env_config["game"])
+        self.env = PrimaiteGymEnv(game=PrimaiteGame.from_config(env_config["cfg"]))
+        self.env.game.episode_counter -= 1
         self.action_space = self.env.action_space
         self.observation_space = self.env.observation_space
 
@@ -92,14 +96,14 @@ class PrimaiteRayEnv(gymnasium.Env):
 class PrimaiteRayMARLEnv(MultiAgentEnv):
     """Ray Environment that inherits from MultiAgentEnv to allow training MARL systems."""
 
-    def __init__(self, env_config: Optional[Dict] = None) -> None:
+    def __init__(self, env_config: Dict) -> None:
         """Initialise the environment.
 
         :param env_config: A dictionary containing the environment configuration. It must contain a single key, `game`
             which is the PrimaiteGame instance.
         :type env_config: Dict[str, PrimaiteGame]
         """
-        self.game: PrimaiteGame = env_config["game"]
+        self.game: PrimaiteGame = PrimaiteGame.from_config(env_config["cfg"])
         """Reference to the primaite game"""
         self.agents: Final[Dict[str, ProxyAgent]] = {agent.agent_name: agent for agent in self.game.rl_agents}
         """List of all possible agents in the environment. This list should not change!"""
@@ -108,7 +112,10 @@ class PrimaiteRayMARLEnv(MultiAgentEnv):
         self.terminateds = set()
         self.truncateds = set()
         self.observation_space = gymnasium.spaces.Dict(
-            {name: agent.observation_manager.space for name, agent in self.agents.items()}
+            {
+                name: gymnasium.spaces.flatten_space(agent.observation_manager.space)
+                for name, agent in self.agents.items()
+            }
         )
         self.action_space = gymnasium.spaces.Dict(
             {name: agent.action_manager.space for name, agent in self.agents.items()}
@@ -159,4 +166,9 @@ class PrimaiteRayMARLEnv(MultiAgentEnv):
 
     def _get_obs(self) -> Dict[str, ObsType]:
         """Return the current observation."""
-        return {name: agent.observation_manager.current_observation for name, agent in self.agents.items()}
+        obs = {}
+        for name, agent in self.agents.items():
+            unflat_space = agent.observation_manager.space
+            unflat_obs = agent.observation_manager.current_observation
+            obs[name] = gymnasium.spaces.flatten(unflat_space, unflat_obs)
+        return obs

src/primaite/session/policy/rllib.py

@@ -12,6 +12,10 @@ from ray import air, tune
 from ray.rllib.algorithms import ppo
 from ray.rllib.algorithms.ppo import PPOConfig
 
+from primaite import getLogger
+
+_LOGGER = getLogger(__name__)
+
 
 class RaySingleAgentPolicy(PolicyABC, identifier="RLLIB_single_agent"):
     """Single agent RL policy using Ray RLLib."""
@@ -19,7 +23,7 @@ class RaySingleAgentPolicy(PolicyABC, identifier="RLLIB_single_agent"):
     def __init__(self, session: "PrimaiteSession", algorithm: Literal["PPO", "A2C"], seed: Optional[int] = None):
         super().__init__(session=session)
 
-        config = {
+        self.config = {
             "env": PrimaiteRayEnv,
             "env_config": {"game": session.game},
             "disable_env_checking": True,
@@ -29,11 +33,12 @@ class RaySingleAgentPolicy(PolicyABC, identifier="RLLIB_single_agent"):
         ray.shutdown()
         ray.init()
 
-        self._algo = ppo.PPO(config=config)
-
     def learn(self, n_episodes: int, timesteps_per_episode: int) -> None:
         """Train the agent."""
-        for ep in range(n_episodes):
+        self.config["training_iterations"] = n_episodes * timesteps_per_episode
+        self.config["train_batch_size"] = 128
+        self._algo = ppo.PPO(config=self.config)
+        _LOGGER.info("Starting RLLIB training session")
         self._algo.train()
 
     def eval(self, n_episodes: int, deterministic: bool) -> None:

src/primaite/session/policy/sb3.py

@@ -51,14 +51,13 @@ class SB3Policy(PolicyABC, identifier="SB3"):
 
     def eval(self, n_episodes: int, deterministic: bool) -> None:
         """Evaluate the agent."""
-        reward_data = evaluate_policy(
+        _ = evaluate_policy(
             self._agent,
             self.session.env,
             n_eval_episodes=n_episodes,
             deterministic=deterministic,
             return_episode_rewards=True,
         )
-        print(reward_data)
 
     def save(self, save_path: Path) -> None:
         """

src/primaite/session/session.py

@@ -62,6 +62,7 @@ class PrimaiteSession:
 
     def start_session(self) -> None:
         """Commence the training/eval session."""
+        print("Starting Primaite Session")
         self.mode = SessionMode.TRAIN
         n_learn_episodes = self.training_options.n_learn_episodes
         n_eval_episodes = self.training_options.n_eval_episodes

src/primaite/simulator/core.py

@@ -113,7 +113,7 @@ class RequestManager(BaseModel):
         """
         if name in self.request_types:
             msg = f"Overwriting request type {name}."
-            _LOGGER.warning(msg)
+            _LOGGER.debug(msg)
 
         self.request_types[name] = request_type
 

src/primaite/simulator/network/container.py

@@ -220,7 +220,7 @@ class Network(SimComponent):
         self._node_id_map[len(self.nodes)] = node
         node.parent = self
         self._nx_graph.add_node(node.hostname)
-        _LOGGER.info(f"Added node {node.uuid} to Network {self.uuid}")
+        _LOGGER.debug(f"Added node {node.uuid} to Network {self.uuid}")
         self._node_request_manager.add_request(name=node.uuid, request_type=RequestType(func=node._request_manager))
 
     def get_node_by_hostname(self, hostname: str) -> Optional[Node]:

src/primaite/simulator/network/hardware/base.py

@@ -181,13 +181,13 @@ class NIC(SimComponent):
         if self.enabled:
             return
         if not self._connected_node:
-            _LOGGER.error(f"NIC {self} cannot be enabled as it is not connected to a Node")
+            _LOGGER.debug(f"NIC {self} cannot be enabled as it is not connected to a Node")
             return
         if self._connected_node.operating_state != NodeOperatingState.ON:
             self._connected_node.sys_log.error(f"NIC {self} cannot be enabled as the endpoint is not turned on")
             return
         if not self._connected_link:
-            _LOGGER.error(f"NIC {self} cannot be enabled as it is not connected to a Link")
+            _LOGGER.debug(f"NIC {self} cannot be enabled as it is not connected to a Link")
             return
 
         self.enabled = True

src/primaite/simulator/system/services/database/database_service.py

@@ -56,7 +56,7 @@ class DatabaseService(Service):
 
     def reset_component_for_episode(self, episode: int):
         """Reset the original state of the SimComponent."""
-        print("Resetting DatabaseService original state on node {self.software_manager.node.hostname}")
+        _LOGGER.debug("Resetting DatabaseService original state on node {self.software_manager.node.hostname}")
         self.connections.clear()
         super().reset_component_for_episode(episode)
 

src/primaite/simulator/system/services/web_server/web_server.py

@@ -47,7 +47,6 @@ class WebServer(Service):
         state["last_response_status_code"] = (
             self.last_response_status_code.value if isinstance(self.last_response_status_code, HttpStatusCode) else None
         )
-        print(state)
         return state
 
     def __init__(self, **kwargs):