#1595:

- SB3 Agent loading
- rename agent.py -> agent_abc.py
- rename hardcoded.py -> hardcoded_abc.py
- Tests
- Added in test asset that is used to load the SB3 Agent

.gitignore

@@ -50,6 +50,9 @@ coverage.xml
 .hypothesis/
 .pytest_cache/
 cover/
+tests/assets/**/*.png
+tests/assets/**/tensorboard_logs/
+tests/assets/**/checkpoints/
 
 # Translations
 *.mo

src/primaite/agents/agent_abc.py

@@ -4,7 +4,7 @@ import json
 from abc import ABC, abstractmethod
 from datetime import datetime
 from pathlib import Path
-from typing import Dict, Final, Union
+from typing import Dict, Optional, Union
 from uuid import uuid4
 
 import yaml
@@ -46,38 +46,63 @@ class AgentSessionABC(ABC):
     """
 
     @abstractmethod
-    def __init__(self, training_config_path, lay_down_config_path):
+    def __init__(
+        self,
+        training_config_path: Optional[Union[str, Path]] = "",
+        lay_down_config_path: Optional[Union[str, Path]] = "",
+        session_path: Optional[Union[str, Path]] = None,
+    ):
         """
-        Initialise an agent session from config files.
+        Initialise an agent session from config files, or load a previous session.
+
+        If training configuration and laydown configuration are provided with a session path,
+        the session path will be used.
 
         :param training_config_path: YAML file containing configurable items defined in
             `primaite.config.training_config.TrainingConfig`
         :type training_config_path: Union[path, str]
         :param lay_down_config_path: YAML file containing configurable items for generating network laydown.
         :type lay_down_config_path: Union[path, str]
+        :param session_path: directory path of the session to load
         """
-        if not isinstance(training_config_path, Path):
+        # initialise variables
-            training_config_path = Path(training_config_path)
-        self._training_config_path: Final[Union[Path, str]] = training_config_path
-        self._training_config: Final[TrainingConfig] = training_config.load(self._training_config_path)
-
-        if not isinstance(lay_down_config_path, Path):
-            lay_down_config_path = Path(lay_down_config_path)
-        self._lay_down_config_path: Final[Union[Path, str]] = lay_down_config_path
-        self._lay_down_config: Dict = lay_down_config.load(self._lay_down_config_path)
-        self.sb3_output_verbose_level = self._training_config.sb3_output_verbose_level
-
         self._env: Primaite
         self._agent = None
         self._can_learn: bool = False
         self._can_evaluate: bool = False
         self.is_eval = False
 
-        self._uuid = str(uuid4())
         self.session_timestamp: datetime = datetime.now()
-        "The session timestamp"
+
-        self.session_path = get_session_path(self.session_timestamp)
+        # convert session to path
-        "The Session path"
+        if session_path is not None:
+            if not isinstance(session_path, Path):
+                session_path = Path(session_path)
+
+            # if a session path is provided, load it
+            if not session_path.exists():
+                raise Exception(f"Session could not be loaded. Path does not exist: {session_path}")
+
+            # load session
+            self.load(session_path)
+        else:
+            # set training config path
+            if not isinstance(training_config_path, Path):
+                training_config_path = Path(training_config_path)
+            self._training_config_path: Union[Path, str] = training_config_path
+            self._training_config: TrainingConfig = training_config.load(self._training_config_path)
+
+            if not isinstance(lay_down_config_path, Path):
+                lay_down_config_path = Path(lay_down_config_path)
+            self._lay_down_config_path: Union[Path, str] = lay_down_config_path
+            self._lay_down_config: Dict = lay_down_config.load(self._lay_down_config_path)
+            self.sb3_output_verbose_level = self._training_config.sb3_output_verbose_level
+
+            # set random UUID for session
+            self._uuid = str(uuid4())
+            "The session timestamp"
+            self.session_path = get_session_path(self.session_timestamp)
+            "The Session path"
 
     @property
     def timestamp_str(self) -> str:
@@ -226,9 +251,7 @@ class AgentSessionABC(ABC):
     def _get_latest_checkpoint(self):
         pass
 
-    @classmethod
+    def load(self, path: Union[str, Path]):
-    @abstractmethod
-    def load(cls, path: Union[str, Path]) -> AgentSessionABC:
         """Load an agent from file."""
         if not isinstance(path, Path):
             path = Path(path)
@@ -252,26 +275,29 @@ class AgentSessionABC(ABC):
             with open(temp_ldc, "w") as file:
                 yaml.dump(md_dict["env"]["lay_down_config"], file)
 
-            agent = cls(temp_tc, temp_ldc)
+            # set training config path
+            self._training_config_path: Union[Path, str] = temp_tc
+            self._training_config: TrainingConfig = training_config.load(self._training_config_path)
+            self._lay_down_config_path: Union[Path, str] = temp_ldc
+            self._lay_down_config: Dict = lay_down_config.load(self._lay_down_config_path)
+            self.sb3_output_verbose_level = self._training_config.sb3_output_verbose_level
 
-            agent.session_path = path
+            # set random UUID for session
+            self._uuid = md_dict["uuid"]
 
-            return agent
+            # set the session path
+            self.session_path = path
+            "The Session path"
 
         else:
             # Session path does not exist
             msg = f"Failed to load PrimAITE Session, path does not exist: {path}"
             _LOGGER.error(msg)
             raise FileNotFoundError(msg)
-        pass
 
     @property
     def _saved_agent_path(self) -> Path:
-        file_name = (
+        file_name = f"{self._training_config.agent_framework}_" f"{self._training_config.agent_identifier}_" f".zip"
-            f"{self._training_config.agent_framework}_"
-            f"{self._training_config.agent_identifier}_"
-            f"{self.timestamp_str}.zip"
-        )
         return self.learning_path / file_name
 
     @abstractmethod

src/primaite/agents/hardcoded_acl.py

@@ -4,7 +4,7 @@ import numpy as np
 
 from primaite.acl.access_control_list import AccessControlList
 from primaite.acl.acl_rule import ACLRule
-from primaite.agents.hardcoded import HardCodedAgentSessionABC
+from primaite.agents.hardcoded_abc import HardCodedAgentSessionABC
 from primaite.agents.utils import (
     get_new_action,
     get_node_of_ip,

src/primaite/agents/hardcoded_node.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from primaite.agents.hardcoded import HardCodedAgentSessionABC
+from primaite.agents.hardcoded_abc import HardCodedAgentSessionABC
 from primaite.agents.utils import get_new_action, transform_action_node_enum, transform_change_obs_readable
 
 

src/primaite/agents/rllib.py

@@ -14,7 +14,7 @@ from ray.tune.logger import UnifiedLogger
 from ray.tune.registry import register_env
 
 from primaite import getLogger
-from primaite.agents.agent import AgentSessionABC
+from primaite.agents.agent_abc import AgentSessionABC
 from primaite.common.enums import AgentFramework, AgentIdentifier
 from primaite.environment.primaite_env import Primaite
 

src/primaite/agents/sb3.py

@@ -1,14 +1,15 @@
 from __future__ import annotations
 
+import json
 from pathlib import Path
-from typing import Union
+from typing import Optional, Union
 
 import numpy as np
 from stable_baselines3 import A2C, PPO
 from stable_baselines3.ppo import MlpPolicy as PPOMlp
 
 from primaite import getLogger
-from primaite.agents.agent import AgentSessionABC
+from primaite.agents.agent_abc import AgentSessionABC
 from primaite.common.enums import AgentFramework, AgentIdentifier
 from primaite.environment.primaite_env import Primaite
 
@@ -18,7 +19,12 @@ _LOGGER = getLogger(__name__)
 class SB3Agent(AgentSessionABC):
     """An AgentSession class that implements a Stable Baselines3 agent."""
 
-    def __init__(self, training_config_path, lay_down_config_path):
+    def __init__(
+        self,
+        training_config_path: Optional[Union[str, Path]] = "",
+        lay_down_config_path: Optional[Union[str, Path]] = "",
+        session_path: Optional[Union[str, Path]] = None,
+    ):
         """
         Initialise the SB3 Agent training session.
 
@@ -31,7 +37,7 @@ class SB3Agent(AgentSessionABC):
         :raises ValueError: If the training config contains an unexpected value for agent_identifies (should be `PPO`
             or `A2C`)
         """
-        super().__init__(training_config_path, lay_down_config_path)
+        super().__init__(training_config_path, lay_down_config_path, session_path)
         if not self._training_config.agent_framework == AgentFramework.SB3:
             msg = f"Expected SB3 agent_framework, " f"got {self._training_config.agent_framework}"
             _LOGGER.error(msg)
@@ -47,7 +53,7 @@ class SB3Agent(AgentSessionABC):
 
         self._tensorboard_log_path = self.learning_path / "tensorboard_logs"
         self._tensorboard_log_path.mkdir(parents=True, exist_ok=True)
-        self._setup()
+
         _LOGGER.debug(
             f"Created {self.__class__.__name__} using: "
             f"agent_framework={self._training_config.agent_framework}, "
@@ -57,22 +63,48 @@ class SB3Agent(AgentSessionABC):
 
         self.is_eval = False
 
+        self._setup()
+
     def _setup(self):
         super()._setup()
+
         self._env = Primaite(
             training_config_path=self._training_config_path,
             lay_down_config_path=self._lay_down_config_path,
             session_path=self.session_path,
             timestamp_str=self.timestamp_str,
         )
-        self._agent = self._agent_class(
+
-            PPOMlp,
+        # check if there is a zip file that needs to be loaded
-            self._env,
+        load_file = next(self.session_path.rglob("*.zip"), None)
-            verbose=self.sb3_output_verbose_level,
+
-            n_steps=self._training_config.num_steps,
+        if not load_file:
-            tensorboard_log=str(self._tensorboard_log_path),
+            # create a new env and agent
-            seed=self._training_config.seed,
+
-        )
+            self._agent = self._agent_class(
+                PPOMlp,
+                self._env,
+                verbose=self.sb3_output_verbose_level,
+                n_steps=self._training_config.num_steps,
+                tensorboard_log=str(self._tensorboard_log_path),
+                seed=self._training_config.seed,
+            )
+        else:
+            # load the file
+            self._agent = self._agent_class.load(load_file)
+
+            # set env values from session metadata
+            with open(self.session_path / "session_metadata.json", "r") as file:
+                md_dict = json.load(file)
+
+            if self.is_eval:
+                # evaluation always starts at 0
+                self._env.episode_count = 0
+                self._env.total_step_count = 0
+            else:
+                # carry on from previous learning sessions
+                self._env.episode_count = md_dict["learning"]["total_episodes"]
+                self._env.total_step_count = md_dict["learning"]["total_time_steps"]
 
     def _save_checkpoint(self):
         checkpoint_n = self._training_config.checkpoint_every_n_episodes
@@ -144,11 +176,6 @@ class SB3Agent(AgentSessionABC):
         self._env.close()
         super().evaluate()
 
-    @classmethod
-    def load(cls, path: Union[str, Path]) -> SB3Agent:
-        """Load an agent from file."""
-        raise NotImplementedError
-
     def save(self):
         """Save the agent."""
         self._agent.save(self._saved_agent_path)

src/primaite/agents/simple.py

@@ -1,4 +1,4 @@
-from primaite.agents.hardcoded import HardCodedAgentSessionABC
+from primaite.agents.hardcoded_abc import HardCodedAgentSessionABC
 from primaite.agents.utils import get_new_action, transform_action_acl_enum, transform_action_node_enum
 
 

src/primaite/primaite_session.py

@@ -5,7 +5,7 @@ from pathlib import Path
 from typing import Dict, Final, Union
 
 from primaite import getLogger
-from primaite.agents.agent import AgentSessionABC
+from primaite.agents.agent_abc import AgentSessionABC
 from primaite.agents.hardcoded_acl import HardCodedACLAgent
 from primaite.agents.hardcoded_node import HardCodedNodeAgent
 from primaite.agents.rllib import RLlibAgent

tests/__init__.py

@@ -4,3 +4,6 @@ from typing import Final
 
 TEST_CONFIG_ROOT: Final[Path] = Path(__file__).parent / "config"
 "The tests config root directory."
+
+TEST_ASSETS_ROOT: Final[Path] = Path(__file__).parent / "assets"
+"The tests assets root directory."

tests/assets/example_sb3_agent_session/evaluation/average_reward_per_episode_2023-07-13_16-18-21.csv

@@ -0,0 +1,26 @@
+Episode,Average Reward
+1,-0.009857999999999992
+2,-0.009857999999999992
+3,-0.009857999999999992
+4,-0.009857999999999992
+5,-0.009857999999999992
+6,-0.009857999999999992
+7,-0.009857999999999992
+8,-0.009857999999999992
+9,-0.009857999999999992
+10,-0.009857999999999992
+11,-0.009857999999999992
+12,-0.009857999999999992
+13,-0.009857999999999992
+14,-0.009857999999999992
+15,-0.009857999999999992
+16,-0.009857999999999992
+17,-0.009857999999999992
+18,-0.009857999999999992
+19,-0.009857999999999992
+20,-0.009857999999999992
+21,-0.009857999999999992
+22,-0.009857999999999992
+23,-0.009857999999999992
+24,-0.009857999999999992
+25,-0.009857999999999992

tests/assets/example_sb3_agent_session/learning/average_reward_per_episode_2023-07-13_16-18-21.csv

@@ -0,0 +1,26 @@
+Episode,Average Reward
+1,-0.009281999999999969
+2,-0.009727999999999978
+3,-0.009469999999999977
+4,-0.009285999999999971
+5,-0.00960599999999997
+6,-0.009449999999999986
+7,-0.009779999999999981
+8,-0.009439999999999974
+9,-0.00967999999999998
+10,-0.008985999999999994
+11,-0.008893999999999982
+12,-0.009083999999999983
+13,-0.008361999999999984
+14,-0.009489999999999964
+15,-0.009027999999999977
+16,-0.009441999999999996
+17,-0.008733999999999988
+18,-0.008675999999999984
+19,-0.008569999999999984
+20,-0.009071999999999988
+21,-0.008043999999999997
+22,-0.007955999999999982
+23,-0.008277999999999976
+24,-0.00803399999999999
+25,-0.00856399999999999

tests/test_session_loading.py

@@ -0,0 +1,100 @@
+import os.path
+import shutil
+import tempfile
+from pathlib import Path
+from typing import Union
+from uuid import uuid4
+
+from primaite import getLogger
+from primaite.agents.sb3 import SB3Agent
+from primaite.common.enums import AgentFramework, AgentIdentifier
+from primaite.utils.session_output_reader import av_rewards_dict
+from tests import TEST_ASSETS_ROOT
+
+_LOGGER = getLogger(__name__)
+
+
+def copy_session_asset(asset_path: Union[str, Path]) -> str:
+    """Copies the asset into a temporary test folder."""
+    if asset_path is None:
+        raise Exception("No path provided")
+
+    if isinstance(asset_path, Path):
+        asset_path = str(os.path.normpath(asset_path))
+
+    copy_path = str(Path(tempfile.gettempdir()) / "primaite" / str(uuid4()))
+
+    # copy the asset into a temp path
+    try:
+        shutil.copytree(asset_path, copy_path)
+    except Exception as e:
+        msg = f"Unable to copy directory: {asset_path}"
+        _LOGGER.error(msg, e)
+        print(msg, e)
+
+    _LOGGER.debug(f"Copied test asset to: {copy_path}")
+
+    # return the copied assets path
+    return copy_path
+
+
+def test_load_sb3_session():
+    """Test that loading an SB3 agent works."""
+    test_path = copy_session_asset(TEST_ASSETS_ROOT / "example_sb3_agent_session")
+
+    loaded_agent = SB3Agent(session_path=test_path)
+
+    # loaded agent should have the same UUID as the previous agent
+    assert loaded_agent.uuid == "8c196c83-b77d-4ef7-af4b-0a3ada30221c"
+    assert loaded_agent._training_config.agent_framework == AgentFramework.SB3.name
+    assert loaded_agent._training_config.agent_identifier == AgentIdentifier.PPO.name
+    assert loaded_agent._training_config.deterministic
+    assert str(loaded_agent.session_path) == str(test_path)
+
+    # run an evaluation
+    loaded_agent.evaluate()
+
+    # load the evaluation average reward csv file
+    eval_mean_reward = av_rewards_dict(
+        loaded_agent.evaluation_path / f"average_reward_per_episode_{loaded_agent.timestamp_str}.csv"
+    )
+
+    # the agent config ran the evaluation in deterministic mode, so should have the same reward value
+    assert len(set(eval_mean_reward.values())) == 1
+
+    # the evaluation should be the same as a previous run
+    assert next(iter(set(eval_mean_reward.values()))) == -0.009857999999999992
+
+    # delete the test directory
+    shutil.rmtree(test_path)
+
+
+def test_load_rllib_session():
+    """Test that loading an RLlib agent works."""
+    # test_path = copy_session_asset(TEST_ASSETS_ROOT)
+    #
+    # loaded_agent = RLlibAgent(session_path=test_path)
+    #
+    # # loaded agent should have the same UUID as the previous agent
+    # assert loaded_agent.uuid == "58c7e648-c784-44e8-bec0-a1db95898270"
+    # assert loaded_agent._training_config.agent_framework == AgentFramework.SB3.name
+    # assert loaded_agent._training_config.agent_identifier == AgentIdentifier.PPO.name
+    # assert loaded_agent._training_config.deterministic
+    # assert str(loaded_agent.session_path) == str(test_path)
+    #
+    # # run an evaluation
+    # loaded_agent.evaluate()
+    #
+    # # load the evaluation average reward csv file
+    # eval_mean_reward = av_rewards_dict(
+    #     loaded_agent.evaluation_path / f"average_reward_per_episode_{loaded_agent.timestamp_str}.csv"
+    # )
+    #
+    # # the agent config ran the evaluation in deterministic mode, so should have the same reward value
+    # assert len(set(eval_mean_reward.values())) == 1
+    #
+    # # the evaluation should be the same as a previous run
+    # assert next(iter(set(eval_mean_reward.values()))) == -0.00011132812500000003
+    #
+    # # delete the test directory
+    # shutil.rmtree(test_path)