-adding eval episodes

assume/common/outputs.py

@@ -9,7 +9,7 @@
 from mango import Role
 from pandas.api.types import is_numeric_dtype
 from sqlalchemy import inspect, text
-from sqlalchemy.exc import OperationalError, ProgrammingError
+from sqlalchemy.exc import DataError, OperationalError, ProgrammingError
 
 logger = logging.getLogger(__name__)
 
@@ -43,6 +43,7 @@ def __init__(
         export_csv_path: str = "",
         save_frequency_hours: int = None,
         learning_mode: bool = False,
+        evaluation_mode: bool = False,
     ):
         super().__init__()
 
@@ -56,16 +57,23 @@ def __init__(
             self.p = Path(self.export_csv_path, simulation_id)
             shutil.rmtree(self.p, ignore_errors=True)
             self.p.mkdir(parents=True)
+
         self.db = db_engine
+
         self.learning_mode = learning_mode
+        self.evaluation_mode = evaluation_mode
 
-        # learning
+        # get episode number if in learning or evaluation mode
         self.episode = None
-        if self.learning_mode:
+        if self.learning_mode or self.evaluation_mode:
             episode = self.simulation_id.split("_")[-1]
             if episode.isdigit():
                 self.episode = int(episode)
 
+            # check if episode=0 and delete all similar runs
+            if self.episode == 0:
+                self.del_similar_runs()
+
         # contruct all timeframe under which hourly values are written to excel and db
         self.start = start
         self.end = end
@@ -99,7 +107,7 @@ def delete_db_scenario(self, simulation_id):
                 logger.debug("deleted %s rows from %s", rowcount, table_name)
 
     def del_similar_runs(self):
-        query = text("select distinct simulation from market_meta")
+        query = text("select distinct simulation from rl_params")
 
         try:
             with self.db.begin() as db:
@@ -173,10 +181,12 @@ def write_rl_params(self, rl_params):
         df = pd.DataFrame.from_records(rl_params, index="datetime")
         if df.empty:
             return
+
         df["simulation"] = self.simulation_id
         df["learning_mode"] = self.learning_mode
-        # get characters after last "_" of simulation id string
+        df["evaluation_mode"] = self.evaluation_mode
         df["episode"] = self.episode
+
         self.write_dfs["rl_params"].append(df)
 
     def write_market_results(self, market_meta):
@@ -357,7 +367,7 @@ async def on_stop(self):
         for query in queries:
             try:
                 df = pd.read_sql(query, self.db)
-            except (OperationalError, ProgrammingError):
+            except (OperationalError, DataError):
                 continue
             except Exception as e:
                 logger.error("could not read query: %s", e)

assume/common/scenario_loader.py

@@ -230,8 +230,10 @@
     inputs_path: str,
     scenario: str,
     study_case: str,
-    disable_learning: bool = False,
+    perform_learning: bool = True,
+    perform_evaluation: bool = False,
     episode: int = 0,
+    eval_episode: int = 0,
     load_learned_path: str = "",
 ):
     """Load a scenario from a given path. Raises: ValueError: If the scenario or study case is not found.
@@ -248,11 +250,10 @@
 
     # load the config file
     path = f"{inputs_path}/{scenario}"
-    with open(f"{path}/config.yaml", "r") as f:
-        config = yaml.safe_load(f)
-        if not study_case:
-            study_case = list(config.keys())[0]
-        config = config[study_case]
+    config = yaml.safe_load(open(f"{path}/config.yaml", "r"))
+    if not study_case:
+        study_case = list(config.keys())[0]
+    config = config[study_case]
     logger.info(f"Starting Scenario {scenario}/{study_case} from {inputs_path}")
 
     world.reset()
@@ -298,8 +299,10 @@
     learning_config: LearningConfig = config.get("learning_config", {})
     bidding_strategy_params = config.get("bidding_strategy_params", {})
 
-    if disable_learning:
-        learning_config["learning_mode"] = False
+    learning_config["learning_mode"] = (
+        config.get("learning_mode", False) and perform_learning
+    )
+    learning_config["evaluation_mode"] = perform_evaluation
 
     if "load_learned_path" not in learning_config.keys():
         if load_learned_path:
@@ -312,6 +315,9 @@
     if learning_config.get("learning_mode", False):
         sim_id = f"{sim_id}_{episode}"
 
+    if learning_config.get("evaluation_mode", False):
+        sim_id = f"{sim_id}_eval_{eval_episode}"
+
     # add forecast provider
     logger.info("Adding forecast")
     forecaster = CsvForecaster(
@@ -515,8 +521,10 @@
     inputs_path: str,
     scenario: str,
     study_case: str,
-    disable_learning: bool = False,
+    perform_learning: bool = True,
+    perform_evaluation: bool = False,
     episode: int = 0,
+    eval_episode: int = 0,
     load_learned_path="",
 ):
     """
@@ -537,8 +545,10 @@
             inputs_path=inputs_path,
             scenario=scenario,
             study_case=study_case,
-            disable_learning=disable_learning,
+            perform_learning=perform_learning,
+            perform_evaluation=perform_evaluation,
             episode=episode,
+            eval_episode=eval_episode,
             load_learned_path=load_learned_path,
         )
     )
@@ -564,28 +574,30 @@
     actors_and_critics = None
     world.output_role.del_similar_runs()
 
+    eval_episode = 1
     for episode in tqdm(
-        range(world.learning_role.training_episodes),
+        range(1, world.learning_role.training_episodes + 1),
         desc="Training Episodes",
     ):
         # TODO normally, loading twice should not create issues, somehow a scheduling issue is raised currently
-        if episode:
+        if episode != 1:
             load_scenario_folder(
                 world,
                 inputs_path,
                 scenario,
                 study_case,
+                perform_learning=True,
                 episode=episode,
-                disable_learning=False,
             )
+
         # give the newly created rl_agent the buffer that we stored from the beginning
         world.learning_role.create_actors_and_critics(
             actors_and_critics=actors_and_critics
         )
         world.learning_role.buffer = buffer
         world.learning_role.episodes_done = episode
 
-        if episode + 1 > world.learning_role.episodes_collecting_initial_experience:
+        if episode > world.learning_role.episodes_collecting_initial_experience:
             world.learning_role.turn_off_initial_exploration()
 
         world.run()
@@ -594,9 +606,10 @@
             world.learning_role.training_episodes,
             world.learning_config.get("validation_episodes_interval", 5),
         )
-        if (episode + 1) % validation_interval == 0 and (
-            episode + 1
-        ) > world.learning_role.episodes_collecting_initial_experience:
+        if (
+            episode % validation_interval == 0
+            and episode > world.learning_role.episodes_collecting_initial_experience
+        ):
             old_path = world.learning_config["load_learned_path"]
             new_path = f"{old_path}_eval"
             # save validation params in validation path
@@ -609,7 +622,9 @@
                 inputs_path,
                 scenario,
                 study_case,
-                disable_learning=True,
+                perform_learning=False,
+                perform_evaluation=True,
+                eval_episode=eval_episode,
                 load_learned_path=new_path,
             )
             world.run()
@@ -620,13 +635,16 @@
                 # save new best params for simulation
                 best_reward = avg_reward
                 world.learning_role.save_params(directory=old_path)
+
+            eval_episode += 1
+
         world.reset()
 
         # in load_scenario_folder_async, we initiate new container and kill old if present
         # as long as we do not skip setup container should be handled correctly
         # if enough initial experience was collected according to specifications in learning config
         # turn off initial exploration and go into full learning mode
-        if episode + 1 >= world.learning_role.episodes_collecting_initial_experience:
+        if episode >= world.learning_role.episodes_collecting_initial_experience:
             world.learning_role.turn_off_initial_exploration()
 
         # container shutdown implicitly with new initialisation
@@ -640,5 +658,5 @@
         inputs_path,
         scenario,
         study_case,
-        disable_learning=True,
+        perform_learning=False,
     )

assume/common/units_operator.py

@@ -398,7 +398,7 @@ def write_learning_to_output(self, start: datetime, marketconfig: MarketConfig):
         db_aid = self.context.data_dict.get("learning_output_agent_id")
         db_addr = self.context.data_dict.get("learning_output_agent_addr")
 
-        if db_aid and db_addr:
+        if db_aid and db_addr and output_agent_list:
             self.context.schedule_instant_acl_message(
                 receiver_id=db_aid,
                 receiver_addr=db_addr,

assume/world.py

@@ -143,8 +143,11 @@ async def setup(
 
     async def setup_learning(self):
         self.bidding_params.update(self.learning_config)
+
         # initiate learning if the learning mode is on and hence we want to learn new strategies
         self.learning_mode = self.learning_config.get("learning_mode", False)
+        self.evaluation_mode = self.learning_config.get("evaluation_mode", False)
+
         if self.learning_mode:
             # if so, we initate the rl learning role with parameters
             from assume.reinforcement_learning.learning_role import Learning
@@ -182,6 +185,7 @@ async def setup_output_agent(self, simulation_id: str, save_frequency_hours: int
             export_csv_path=self.export_csv_path,
             save_frequency_hours=save_frequency_hours,
             learning_mode=self.learning_mode,
+            evaluation_mode=self.evaluation_mode,
         )
         if self.same_process:
             output_agent = RoleAgent(
@@ -325,14 +329,14 @@ def add_market_operator(
         market_operator_agent.markets = []
 
         # after creation of an agent - we set additional context params
-        market_operator_agent._role_context.data_dict = {
-            "output_agent_addr": None
-            if self.learning_mode
-            else self.output_agent_addr[0],
-            "output_agent_id": None
-            if self.learning_mode
-            else self.output_agent_addr[1],
-        }
+        market_operator_agent._role_context.data_dict = {}
+        if not self.learning_mode and not self.evaluation_mode:
+            market_operator_agent._role_context.data_dict.update(
+                {
+                    "output_agent_addr": self.output_agent_addr[0],
+                    "output_agent_id": self.output_agent_addr[1],
+                }
+            )
         self.market_operators[id] = market_operator_agent
 
     def add_market(