#917 - Almost there. All output files being writen for SB3/RLLIB PPO & A2C. Just need to bring in the hardcoded agents then update the testa and docs.

src/primaite/VERSION

@@ -1 +1 @@
-2.0.0rc1
+2.0.0b1

src/primaite/agents/agent.py

@@ -5,19 +5,18 @@ from pathlib import Path
 from typing import Optional, Final, Dict, Union, List
 from uuid import uuid4
 
-from primaite import getLogger
+from primaite import getLogger, SESSIONS_DIR
 from primaite.common.enums import OutputVerboseLevel
 from primaite.config import lay_down_config
 from primaite.config import training_config
 from primaite.config.training_config import TrainingConfig
 from primaite.environment.primaite_env import Primaite
-from primaite.transactions.transactions_to_file import \
+
-    write_transaction_to_file
 
 _LOGGER = getLogger(__name__)
 
 
-def _get_temp_session_path(session_timestamp: datetime) -> Path:
+def _get_session_path(session_timestamp: datetime) -> Path:
     """
     Get a temp directory session path the test session will output to.
 
@@ -26,7 +25,7 @@ def _get_temp_session_path(session_timestamp: datetime) -> Path:
     """
     date_dir = session_timestamp.strftime("%Y-%m-%d")
     session_path = session_timestamp.strftime("%Y-%m-%d_%H-%M-%S")
-    session_path = Path("./") / date_dir / session_path
+    session_path = SESSIONS_DIR / date_dir / session_path
     session_path.mkdir(exist_ok=True, parents=True)
 
     return session_path
@@ -57,16 +56,16 @@ class AgentSessionABC(ABC):
 
         self._env: Primaite
         self._agent = None
-        self._transaction_list: List[Dict] = []
         self._can_learn: bool = False
         self._can_evaluate: bool = False
 
         self._uuid = str(uuid4())
         self.session_timestamp: datetime = datetime.now()
         "The session timestamp"
-        self.session_path = _get_temp_session_path(self.session_timestamp)
+        self.session_path = _get_session_path(self.session_timestamp)
         "The Session path"
         self.checkpoints_path = self.session_path / "checkpoints"
+        self.checkpoints_path.mkdir(parents=True, exist_ok=True)
         "The Session checkpoints path"
 
         self.timestamp_str = self.session_timestamp.strftime(
@@ -167,11 +166,6 @@ class AgentSessionABC(ABC):
     ):
         if self._can_learn:
             _LOGGER.debug("Writing transactions")
-            write_transaction_to_file(
-                transaction_list=self._transaction_list,
-                session_path=self.session_path,
-                timestamp_str=self.timestamp_str,
-            )
             self._update_session_metadata_file()
             self._can_evaluate = True
 

src/primaite/agents/rllib.py

@@ -1,18 +1,23 @@
-import glob
+import json
-import time
+from datetime import datetime
-from enum import Enum
 from pathlib import Path
-from typing import Union, Optional
+from pathlib import Path
+from typing import Optional
 
 from ray.rllib.algorithms import Algorithm
 from ray.rllib.algorithms.ppo import PPOConfig
+from ray.rllib.algorithms.a2c import A2CConfig
+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.config import training_config
+from primaite.common.enums import AgentFramework, RedAgentIdentifier
 from primaite.environment.primaite_env import Primaite
 
 
+_LOGGER = getLogger(__name__)
+
 def _env_creator(env_config):
     return Primaite(
         training_config_path=env_config["training_config_path"],
@@ -23,7 +28,17 @@ def _env_creator(env_config):
     )
 
 
-class RLlibPPO(AgentSessionABC):
+def _custom_log_creator(session_path: Path):
+    logdir = session_path / "ray_results"
+    logdir.mkdir(parents=True, exist_ok=True)
+
+    def logger_creator(config):
+        return UnifiedLogger(config, logdir, loggers=None)
+
+    return logger_creator
+
+
+class RLlibAgent(AgentSessionABC):
 
     def __init__(
             self,
@@ -31,17 +46,63 @@ class RLlibPPO(AgentSessionABC):
             lay_down_config_path
     ):
         super().__init__(training_config_path, lay_down_config_path)
-        self._ppo_config: PPOConfig
+        if not self._training_config.agent_framework == AgentFramework.RLLIB:
+            msg = (f"Expected RLLIB agent_framework, "
+                   f"got {self._training_config.agent_framework}")
+            _LOGGER.error(msg)
+            raise ValueError(msg)
+        if self._training_config.red_agent_identifier == RedAgentIdentifier.PPO:
+            self._agent_config_class = PPOConfig
+        elif self._training_config.red_agent_identifier == RedAgentIdentifier.A2C:
+            self._agent_config_class = A2CConfig
+        else:
+            msg = ("Expected PPO or A2C red_agent_identifier, "
+                   f"got {self._training_config.red_agent_identifier.value}")
+            _LOGGER.error(msg)
+            raise ValueError(msg)
+        self._agent_config: PPOConfig
+
         self._current_result: dict
         self._setup()
-        self._agent.save()
+        _LOGGER.debug(
+            f"Created {self.__class__.__name__} using: "
+            f"agent_framework={self._training_config.agent_framework}, "
+            f"red_agent_identifier="
+            f"{self._training_config.red_agent_identifier}, "
+            f"deep_learning_framework="
+            f"{self._training_config.deep_learning_framework}"
+        )
+
+    def _update_session_metadata_file(self):
+        """
+        Update the ``session_metadata.json`` file.
+
+        Updates the `session_metadata.json`` in the ``session_path`` directory
+        with the following key/value pairs:
+
+        - end_datetime: The date & time the session ended in iso format.
+        - total_episodes: The total number of training episodes completed.
+        - total_time_steps: The total number of training time steps completed.
+        """
+        with open(self.session_path / "session_metadata.json", "r") as file:
+            metadata_dict = json.load(file)
+
+        metadata_dict["end_datetime"] = datetime.now().isoformat()
+        metadata_dict["total_episodes"] = self._current_result["episodes_total"]
+        metadata_dict["total_time_steps"] = self._current_result["timesteps_total"]
+
+        filepath = self.session_path / "session_metadata.json"
+        _LOGGER.debug(f"Updating Session Metadata file: {filepath}")
+        with open(filepath, "w") as file:
+            json.dump(metadata_dict, file)
+            _LOGGER.debug("Finished updating session metadata file")
 
     def _setup(self):
         super()._setup()
         register_env("primaite", _env_creator)
-        self._ppo_config = PPOConfig()
+        self._agent_config = self._agent_config_class()
 
-        self._ppo_config.environment(
+        self._agent_config.environment(
             env="primaite",
             env_config=dict(
                 training_config_path=self._training_config_path,
@@ -52,19 +113,21 @@ class RLlibPPO(AgentSessionABC):
             )
         )
 
-        self._ppo_config.training(
+        self._agent_config.training(
             train_batch_size=self._training_config.num_steps
         )
-        self._ppo_config.framework(
+        self._agent_config.framework(
-            framework=self._training_config.deep_learning_framework.value
+            framework=self._training_config.deep_learning_framework
         )
 
-        self._ppo_config.rollouts(
+        self._agent_config.rollouts(
             num_rollout_workers=1,
             num_envs_per_worker=1,
             horizon=self._training_config.num_steps
         )
-        self._agent: Algorithm = self._ppo_config.build()
+        self._agent: Algorithm = self._agent_config.build(
+            logger_creator=_custom_log_creator(self.session_path)
+        )
 
     def _save_checkpoint(self):
         checkpoint_n = self._training_config.checkpoint_every_n_episodes
@@ -84,8 +147,8 @@ class RLlibPPO(AgentSessionABC):
     ):
         # Temporarily override train_batch_size and horizon
         if time_steps:
-            self._ppo_config.train_batch_size = time_steps
+            self._agent_config.train_batch_size = time_steps
-            self._ppo_config.horizon = time_steps
+            self._agent_config.horizon = time_steps
 
         if not episodes:
             episodes = self._training_config.num_episodes

src/primaite/agents/sb3.py

@@ -1,23 +1,48 @@
 from typing import Optional
 
 import numpy as np
-from stable_baselines3 import PPO
+from stable_baselines3 import PPO, A2C
 
+from primaite import getLogger
 from primaite.agents.agent import AgentSessionABC
+from primaite.common.enums import RedAgentIdentifier, AgentFramework
 from primaite.environment.primaite_env import Primaite
 from stable_baselines3.ppo import MlpPolicy as PPOMlp
 
+_LOGGER = getLogger(__name__)
 
-class SB3PPO(AgentSessionABC):
+
+class SB3Agent(AgentSessionABC):
     def __init__(
             self,
             training_config_path,
             lay_down_config_path
     ):
         super().__init__(training_config_path, lay_down_config_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)
+            raise ValueError(msg)
+        if self._training_config.red_agent_identifier == RedAgentIdentifier.PPO:
+            self._agent_class = PPO
+        elif self._training_config.red_agent_identifier == RedAgentIdentifier.A2C:
+            self._agent_class = A2C
+        else:
+            msg = ("Expected PPO or A2C red_agent_identifier, "
+                   f"got {self._training_config.red_agent_identifier.value}")
+            _LOGGER.error(msg)
+            raise ValueError(msg)
+
         self._tensorboard_log_path = self.session_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}, "
+            f"red_agent_identifier="
+            f"{self._training_config.red_agent_identifier}"
+        )
 
     def _setup(self):
         super()._setup()
@@ -28,10 +53,10 @@ class SB3PPO(AgentSessionABC):
             session_path=self.session_path,
             timestamp_str=self.timestamp_str
         )
-        self._agent = PPO(
+        self._agent = self._agent_class(
             PPOMlp,
             self._env,
-            verbose=1,
+            verbose=self._training_config.output_verbose_level,
             n_steps=self._training_config.num_steps,
             tensorboard_log=self._tensorboard_log_path
         )
@@ -65,6 +90,7 @@ class SB3PPO(AgentSessionABC):
         for i in range(episodes):
             self._agent.learn(total_timesteps=time_steps)
             self._save_checkpoint()
+        self._env.close()
         super().learn()
 
     def evaluate(

src/primaite/common/enums.py

@@ -80,13 +80,13 @@ class Protocol(Enum):
 
 
 class SessionType(Enum):
-    "The type of PrimAITE Session to be run."
+    """The type of PrimAITE Session to be run."""
     TRAINING = 1
     EVALUATION = 2
     BOTH = 3
 
 
-class VerboseLevel(Enum):
+class VerboseLevel(IntEnum):
     """PrimAITE Session Output verbose level."""
     NO_OUTPUT = 0
     INFO = 1

src/primaite/config/_package_data/lay_down/lay_down_config_1_DDOS_basic.yaml

@@ -1,7 +1,3 @@
-- item_type: ACTIONS
-  type: NODE
-- item_type: STEPS
-  steps: 128
 - item_type: PORTS
   ports_list:
     - port: '80'

src/primaite/config/_package_data/lay_down/lay_down_config_2_DDOS_basic.yaml

@@ -1,7 +1,3 @@
-- item_type: ACTIONS
-  type: NODE
-- item_type: STEPS
-  steps: 128
 - item_type: PORTS
   ports_list:
     - port: '80'

src/primaite/config/_package_data/lay_down/lay_down_config_3_DOS_very_basic.yaml

@@ -1,7 +1,3 @@
-- item_type: ACTIONS
-  type: NODE
-- item_type: STEPS
-  steps: 256
 - item_type: PORTS
   ports_list:
     - port: '80'

src/primaite/config/_package_data/training/training_config_main.yaml

@@ -59,7 +59,7 @@ observation_space_high_value: 1000000000
 # "NONE" (No Output)
 # "INFO" (Info Messages)
 # "ALL" (All Messages)
-output_verbose_level: INFO
+output_verbose_level: NONE
 
 # Reward values
 # Generic

src/primaite/config/training_config.py

@@ -185,7 +185,6 @@ class TrainingConfig:
         for field, enum_class in field_enum_map.items():
             if field in config_dict:
                 config_dict[field] = enum_class[config_dict[field]]
-
         return TrainingConfig(**config_dict)
 
     def to_dict(self, json_serializable: bool = True):
@@ -203,6 +202,7 @@ class TrainingConfig:
             data["red_agent_identifier"] = self.red_agent_identifier.value
             data["action_type"] = self.action_type.value
             data["output_verbose_level"] = self.output_verbose_level.value
+            data["session_type"] = self.session_type.value
 
         return data
 

src/primaite/environment/primaite_env.py

@@ -45,6 +45,8 @@ from primaite.pol.ier import IER
 from primaite.pol.red_agent_pol import apply_red_agent_iers, \
     apply_red_agent_node_pol
 from primaite.transactions.transaction import Transaction
+from primaite.transactions.transactions_to_file import \
+    write_transaction_to_file
 
 _LOGGER = logging.getLogger(__name__)
 _LOGGER.setLevel(logging.INFO)
@@ -407,10 +409,19 @@ class Primaite(Env):
         # Return
         return self.env_obs, reward, done, self.step_info
 
-    def __close__(self):
+    def close(self):
-        """Override close function."""
+        self.__close__()
-        self.csv_file.close()
 
+    def __close__(self):
+        """
+        Override close function
+        """
+        write_transaction_to_file(
+            self.transaction_list,
+            self.session_path,
+            self.timestamp_str
+        )
+        self.csv_file.close()
     def init_acl(self):
         """Initialise the Access Control List."""
         self.acl.remove_all_rules()

src/primaite/main.py

@@ -1,229 +1,162 @@
-# # Crown Copyright (C) Dstl 2022. DEFCON 703. Shared in confidence.
+# Crown Copyright (C) Dstl 2022. DEFCON 703. Shared in confidence.
-# """
+"""
-# The main PrimAITE session runner module.
+The main PrimAITE session runner module.
-#
+
-# TODO: This will eventually be refactored out into a proper Session class.
+TODO: This will eventually be refactored out into a proper Session class.
-# TODO: The passing about of session_path and timestamp_str is temporary and
+TODO: The passing about of session_path and timestamp_str is temporary and
-#     will be cleaned up once we move to a proper Session class.
+    will be cleaned up once we move to a proper Session class.
-# """
+"""
-# import argparse
+import argparse
-# import json
+import json
-# import time
+import time
-# from datetime import datetime
+from datetime import datetime
-# from pathlib import Path
+from pathlib import Path
-# from typing import Final, Union
+from typing import Final, Union
-# from uuid import uuid4
+from uuid import uuid4
-#
+
-# from stable_baselines3 import A2C, PPO
+from stable_baselines3 import A2C, PPO
-# from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.evaluation import evaluate_policy
-# from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
+from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
-# from stable_baselines3.ppo import MlpPolicy as PPOMlp
+from stable_baselines3.ppo import MlpPolicy as PPOMlp
-#
+
-# from primaite import SESSIONS_DIR, getLogger
+from primaite import SESSIONS_DIR, getLogger
-# from primaite.config.training_config import TrainingConfig
+from primaite.config.training_config import TrainingConfig
-# from primaite.environment.primaite_env import Primaite
+from primaite.environment.primaite_env import Primaite
-# from primaite.transactions.transactions_to_file import \
+from primaite.primaite_session import PrimaiteSession
-#     write_transaction_to_file
+from primaite.transactions.transactions_to_file import \
-#
+    write_transaction_to_file
-# _LOGGER = getLogger(__name__)
+
-#
+_LOGGER = getLogger(__name__)
-#
+
-# def run_generic(env: Primaite, config_values: TrainingConfig):
+
-#     """
+def run_generic(env: Primaite, config_values: TrainingConfig):
-#     Run against a generic agent.
+    """
-#
+    Run against a generic agent.
-#     :param env: An instance of
+
-#         :class:`~primaite.environment.primaite_env.Primaite`.
+    :param env: An instance of
-#     :param config_values: An instance of
+        :class:`~primaite.environment.primaite_env.Primaite`.
-#         :class:`~primaite.config.training_config.TrainingConfig`.
+    :param config_values: An instance of
-#     """
+        :class:`~primaite.config.training_config.TrainingConfig`.
-#     for episode in range(0, config_values.num_episodes):
+    """
-#         env.reset()
+    for episode in range(0, config_values.num_episodes):
-#         for step in range(0, config_values.num_steps):
+        env.reset()
-#             # Send the observation space to the agent to get an action
+        for step in range(0, config_values.num_steps):
-#             # TEMP - random action for now
+            # Send the observation space to the agent to get an action
-#             # action = env.blue_agent_action(obs)
+            # TEMP - random action for now
-#             action = env.action_space.sample()
+            # action = env.blue_agent_action(obs)
-#
+            action = env.action_space.sample()
-#             # Run the simulation step on the live environment
+
-#             obs, reward, done, info = env.step(action)
+            # Run the simulation step on the live environment
-#
+            obs, reward, done, info = env.step(action)
-#             # Break if done is True
+
-#             if done:
+            # Break if done is True
-#                 break
+            if done:
-#
+                break
-#             # Introduce a delay between steps
+
-#             time.sleep(config_values.time_delay / 1000)
+            # Introduce a delay between steps
-#
+            time.sleep(config_values.time_delay / 1000)
-#         # Reset the environment at the end of the episode
+
-#
+        # Reset the environment at the end of the episode
-#     env.close()
+
-#
+    env.close()
-#
+
-# def run_stable_baselines3_ppo(
+
-#     env: Primaite, config_values: TrainingConfig, session_path: Path, timestamp_str: str
+def run_stable_baselines3_ppo(
-# ):
+    env: Primaite, config_values: TrainingConfig, session_path: Path, timestamp_str: str
-#     """
+):
-#     Run against a stable_baselines3 PPO agent.
+    """
-#
+    Run against a stable_baselines3 PPO agent.
-#     :param env: An instance of
+
-#         :class:`~primaite.environment.primaite_env.Primaite`.
+    :param env: An instance of
-#     :param config_values: An instance of
+        :class:`~primaite.environment.primaite_env.Primaite`.
-#         :class:`~primaite.config.training_config.TrainingConfig`.
+    :param config_values: An instance of
-#     :param session_path: The directory path the session is writing to.
+        :class:`~primaite.config.training_config.TrainingConfig`.
-#     :param timestamp_str: The session timestamp in the format:
+    :param session_path: The directory path the session is writing to.
-#         <yyyy-mm-dd>_<hh-mm-ss>.
+    :param timestamp_str: The session timestamp in the format:
-#     """
+        <yyyy-mm-dd>_<hh-mm-ss>.
-#     if config_values.load_agent:
+    """
-#         try:
+    if config_values.load_agent:
-#             agent = PPO.load(
+        try:
-#                 config_values.agent_load_file,
+            agent = PPO.load(
-#                 env,
+                config_values.agent_load_file,
-#                 verbose=0,
+                env,
-#                 n_steps=config_values.num_steps,
+                verbose=0,
-#             )
+                n_steps=config_values.num_steps,
-#         except Exception:
+            )
-#             print(
+        except Exception:
-#                 "ERROR: Could not load agent at location: "
+            print(
-#                 + config_values.agent_load_file
+                "ERROR: Could not load agent at location: "
-#             )
+                + config_values.agent_load_file
-#             _LOGGER.error("Could not load agent")
+            )
-#             _LOGGER.error("Exception occured", exc_info=True)
+            _LOGGER.error("Could not load agent")
-#     else:
+            _LOGGER.error("Exception occured", exc_info=True)
-#         agent = PPO(PPOMlp, env, verbose=0, n_steps=config_values.num_steps)
+    else:
-#
+        agent = PPO(PPOMlp, env, verbose=0, n_steps=config_values.num_steps)
-#     if config_values.session_type == "TRAINING":
+
-#         # We're in a training session
+    if config_values.session_type == "TRAINING":
-#         print("Starting training session...")
+        # We're in a training session
-#         _LOGGER.debug("Starting training session...")
+        print("Starting training session...")
-#         for episode in range(config_values.num_episodes):
+        _LOGGER.debug("Starting training session...")
-#             agent.learn(total_timesteps=config_values.num_steps)
+        for episode in range(config_values.num_episodes):
-#         _save_agent(agent, session_path, timestamp_str)
+            agent.learn(total_timesteps=config_values.num_steps)
-#     else:
+        _save_agent(agent, session_path, timestamp_str)
-#         # Default to being in an evaluation session
+    else:
-#         print("Starting evaluation session...")
+        # Default to being in an evaluation session
-#         _LOGGER.debug("Starting evaluation session...")
+        print("Starting evaluation session...")
-#         evaluate_policy(agent, env, n_eval_episodes=config_values.num_episodes)
+        _LOGGER.debug("Starting evaluation session...")
-#
+        evaluate_policy(agent, env, n_eval_episodes=config_values.num_episodes)
-#     env.close()
+
-#
+    env.close()
-#
+
-#
+
-#
+
-# def _save_agent(agent: OnPolicyAlgorithm, session_path: Path, timestamp_str: str):
+
-#     """
+def _save_agent(agent: OnPolicyAlgorithm, session_path: Path, timestamp_str: str):
-#     Persist an agent.
+    """
-#
+    Persist an agent.
-#     Only works for stable baselines3 agents at present.
+
-#
+    Only works for stable baselines3 agents at present.
-#     :param session_path: The directory path the session is writing to.
+
-#     :param timestamp_str: The session timestamp in the format:
+    :param session_path: The directory path the session is writing to.
-#         <yyyy-mm-dd>_<hh-mm-ss>.
+    :param timestamp_str: The session timestamp in the format:
-#     """
+        <yyyy-mm-dd>_<hh-mm-ss>.
-#     if not isinstance(agent, OnPolicyAlgorithm):
+    """
-#         msg = f"Can only save {OnPolicyAlgorithm} agents, got {type(agent)}."
+    if not isinstance(agent, OnPolicyAlgorithm):
-#         _LOGGER.error(msg)
+        msg = f"Can only save {OnPolicyAlgorithm} agents, got {type(agent)}."
-#     else:
+        _LOGGER.error(msg)
-#         filepath = session_path / f"agent_saved_{timestamp_str}"
+    else:
-#         agent.save(filepath)
+        filepath = session_path / f"agent_saved_{timestamp_str}"
-#         _LOGGER.debug(f"Trained agent saved as: {filepath}")
+        agent.save(filepath)
-#
+        _LOGGER.debug(f"Trained agent saved as: {filepath}")
-#
+
-#
+
-#
+
-# def run(training_config_path: Union[str, Path], lay_down_config_path: Union[str, Path]):
+
-#     """Run the PrimAITE Session.
+def run(training_config_path: Union[str, Path], lay_down_config_path: Union[str, Path]):
-#
+    """Run the PrimAITE Session.
-#     :param training_config_path: The training config filepath.
+
-#     :param lay_down_config_path: The lay down config filepath.
+    :param training_config_path: The training config filepath.
-#     """
+    :param lay_down_config_path: The lay down config filepath.
-#     # Welcome message
+    """
-#     print("Welcome to the Primary-level AI Training Environment (PrimAITE)")
+    session = PrimaiteSession(training_config_path, lay_down_config_path)
-#     uuid = str(uuid4())
+
-#     session_timestamp: Final[datetime] = datetime.now()
+    session.setup()
-#     session_path = _get_session_path(session_timestamp)
+    session.learn()
-#     timestamp_str = session_timestamp.strftime("%Y-%m-%d_%H-%M-%S")
+
-#
+
-#     print(f"The output directory for this session is: {session_path}")
+if __name__ == "__main__":
-#
+    parser = argparse.ArgumentParser()
-#     # Create a list of transactions
+    parser.add_argument("--tc")
-#     # A transaction is an object holding the:
+    parser.add_argument("--ldc")
-#     # - episode #
+    args = parser.parse_args()
-#     # - step #
+    if not args.tc:
-#     # - initial observation space
+        _LOGGER.error(
-#     # - action
+            "Please provide a training config file using the --tc " "argument"
-#     # - reward
+        )
-#     # - new observation space
+    if not args.ldc:
-#     transaction_list = []
+        _LOGGER.error(
-#
+            "Please provide a lay down config file using the --ldc " "argument"
-#     # Create the Primaite environment
+        )
-#     env = Primaite(
+    run(training_config_path=args.tc, lay_down_config_path=args.ldc)
-#         training_config_path=training_config_path,
+
-#         lay_down_config_path=lay_down_config_path,
+
-#         transaction_list=transaction_list,
-#         session_path=session_path,
-#         timestamp_str=timestamp_str,
-#     )
-#
-#     print("Writing Session Metadata file...")
-#
-#     _write_session_metadata_file(
-#         session_path=session_path, uuid=uuid, session_timestamp=session_timestamp, env=env
-#     )
-#
-#     config_values = env.training_config
-#
-#     # Get the number of steps (which is stored in the child config file)
-#     config_values.num_steps = env.episode_steps
-#
-#     # Run environment against an agent
-#     if config_values.agent_identifier == "GENERIC":
-#         run_generic(env=env, config_values=config_values)
-#     elif config_values.agent_identifier == "STABLE_BASELINES3_PPO":
-#         run_stable_baselines3_ppo(
-#             env=env,
-#             config_values=config_values,
-#             session_path=session_path,
-#             timestamp_str=timestamp_str,
-#         )
-#     elif config_values.agent_identifier == "STABLE_BASELINES3_A2C":
-#         run_stable_baselines3_a2c(
-#             env=env,
-#             config_values=config_values,
-#             session_path=session_path,
-#             timestamp_str=timestamp_str,
-#         )
-#
-#     print("Session finished")
-#     _LOGGER.debug("Session finished")
-#
-#     print("Saving transaction logs...")
-#     write_transaction_to_file(
-#         transaction_list=transaction_list,
-#         session_path=session_path,
-#         timestamp_str=timestamp_str,
-#     )
-#
-#     print("Updating Session Metadata file...")
-#     _update_session_metadata_file(session_path=session_path, env=env)
-#
-#     print("Finished")
-#     _LOGGER.debug("Finished")
-#
-#
-# if __name__ == "__main__":
-#     parser = argparse.ArgumentParser()
-#     parser.add_argument("--tc")
-#     parser.add_argument("--ldc")
-#     args = parser.parse_args()
-#     if not args.tc:
-#         _LOGGER.error(
-#             "Please provide a training config file using the --tc " "argument"
-#         )
-#     if not args.ldc:
-#         _LOGGER.error(
-#             "Please provide a lay down config file using the --ldc " "argument"
-#         )
-#     run(training_config_path=args.tc, lay_down_config_path=args.ldc)
-#
-#

src/primaite/primaite_session.py

@@ -8,10 +8,10 @@ from uuid import uuid4
 
 from primaite import getLogger, SESSIONS_DIR
 from primaite.agents.agent import AgentSessionABC
-from primaite.agents.rllib import RLlibPPO
+from primaite.agents.rllib import RLlibAgent
-from primaite.agents.sb3 import SB3PPO
+from primaite.agents.sb3 import SB3Agent
 from primaite.common.enums import AgentFramework, RedAgentIdentifier, \
-    ActionType
+    ActionType, SessionType
 from primaite.config import lay_down_config, training_config
 from primaite.config.training_config import TrainingConfig
 from primaite.environment.primaite_env import Primaite
@@ -95,35 +95,19 @@ class PrimaiteSession:
                 pass
 
         elif self._training_config.agent_framework == AgentFramework.SB3:
-            if self._training_config.red_agent_identifier == RedAgentIdentifier.PPO:
+            # Stable Baselines3 Agent
-                # Stable Baselines3/Proximal Policy Optimization
+            self._agent_session = SB3Agent(
-                self._agent_session = SB3PPO(
+                self._training_config_path,
-                    self._training_config_path,
+                self._lay_down_config_path
-                    self._lay_down_config_path
+            )
-                )
-
-            elif self._training_config.red_agent_identifier == RedAgentIdentifier.A2C:
-                # Stable Baselines3/Advantage Actor Critic
-                raise NotImplementedError
-            else:
-                # Invalid AgentFramework RedAgentIdentifier combo
-                pass
 
         elif self._training_config.agent_framework == AgentFramework.RLLIB:
-            if self._training_config.red_agent_identifier == RedAgentIdentifier.PPO:
+            # Ray RLlib Agent
-                # Ray RLlib/Proximal Policy Optimization
+            self._agent_session = RLlibAgent(
-                self._agent_session = RLlibPPO(
+                self._training_config_path,
-                    self._training_config_path,
+                self._lay_down_config_path
-                    self._lay_down_config_path
+            )
-                )
 
-            elif self._training_config.red_agent_identifier == RedAgentIdentifier.A2C:
-                # Ray RLlib/Advantage Actor Critic
-                raise NotImplementedError
-
-            else:
-                # Invalid AgentFramework RedAgentIdentifier combo
-                pass
         else:
             # Invalid AgentFramework
             pass
@@ -134,7 +118,8 @@ class PrimaiteSession:
             episodes: Optional[int] = None,
             **kwargs
     ):
-        self._agent_session.learn(time_steps, episodes, **kwargs)
+        if not self._training_config.session_type == SessionType.EVALUATION:
+            self._agent_session.learn(time_steps, episodes, **kwargs)
 
     def evaluate(
             self,
@@ -142,18 +127,5 @@ class PrimaiteSession:
             episodes: Optional[int] = None,
             **kwargs
     ):
-        self._agent_session.evaluate(time_steps, episodes, **kwargs)
+        if not self._training_config.session_type == SessionType.TRAINING:
-
+            self._agent_session.evaluate(time_steps, episodes, **kwargs)
-    @classmethod
-    def import_agent(
-            cls,
-            gent_path: str,
-            training_config_path: str,
-            lay_down_config_path: str
-    ) -> PrimaiteSession:
-        session = PrimaiteSession(training_config_path, lay_down_config_path)
-
-        # Reset the UUID
-        session._uuid = ""
-
-        return session

src/primaite/transactions/transactions_to_file.py

@@ -4,6 +4,8 @@
 import csv
 from pathlib import Path
 
+import numpy as np
+
 from primaite import getLogger
 
 _LOGGER = getLogger(__name__)
@@ -54,8 +56,12 @@ def write_transaction_to_file(transaction_list, session_path: Path, timestamp_st
     # Label the obs space fields in csv as "OSI_1_1", "OSN_1_1" and action
     # space as "AS_1"
     # This will be tied into the PrimAITE Use Case so that they make sense
+
     template_transation = transaction_list[0]
-    action_length = template_transation.action_space.size
+    if isinstance(template_transation.action_space, int):
+        action_length = template_transation.action_space
+    else:
+        action_length = template_transation.action_space.size
     obs_shape = template_transation.obs_space_post.shape
     obs_assets = template_transation.obs_space_post.shape[0]
     if len(obs_shape) == 1: