#1595: test to make sure that the loaded agent trains + remove unnecessary files + fixing agent save output name

src/primaite/agents/agent_abc.py

@@ -297,7 +297,7 @@ class AgentSessionABC(ABC):
 
     @property
     def _saved_agent_path(self) -> Path:
-        file_name = f"{self._training_config.agent_framework}_" f"{self._training_config.agent_identifier}_" f".zip"
+        file_name = f"{self._training_config.agent_framework}_" f"{self._training_config.agent_identifier}" f".zip"
         return self.learning_path / file_name
 
     @abstractmethod

src/primaite/agents/sb3.py

@@ -66,8 +66,7 @@ class SB3Agent(AgentSessionABC):
         self._setup()
 
     def _setup(self):
-        super()._setup()
+        """Set up the SB3 Agent."""
-
         self._env = Primaite(
             training_config_path=self._training_config_path,
             lay_down_config_path=self._lay_down_config_path,
@@ -85,18 +84,16 @@ class SB3Agent(AgentSessionABC):
                 PPOMlp,
                 self._env,
                 verbose=self.sb3_output_verbose_level,
-                n_steps=self._training_config.num_steps,
+                n_steps=self._training_config.num_train_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)
 
+            # load environment values
             if self.is_eval:
                 # evaluation always starts at 0
                 self._env.episode_count = 0
@@ -106,6 +103,15 @@ class SB3Agent(AgentSessionABC):
                 self._env.episode_count = md_dict["learning"]["total_episodes"]
                 self._env.total_step_count = md_dict["learning"]["total_time_steps"]
 
+            # load the file
+            self._agent = self._agent_class.load(load_file, env=self._env)
+
+            # set agent values
+            self._agent.verbose = self.sb3_output_verbose_level
+            self._agent.tensorboard_log = self.session_path / "learning/tensorboard_logs"
+
+        super()._setup()
+
     def _save_checkpoint(self):
         checkpoint_n = self._training_config.checkpoint_every_n_episodes
         episode_count = self._env.episode_count

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

@@ -1,26 +0,0 @@
-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

@@ -1,26 +0,0 @@
-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

@@ -40,17 +40,42 @@ def copy_session_asset(asset_path: Union[str, Path]) -> str:
 
 def test_load_sb3_session():
     """Test that loading an SB3 agent works."""
+    expected_learn_mean_reward_per_episode = {
+        10: 0,
+        11: -0.008037109374999995,
+        12: -0.007978515624999988,
+        13: -0.008191406249999991,
+        14: -0.00817578124999999,
+        15: -0.008085937499999998,
+        16: -0.007837890624999982,
+        17: -0.007798828124999992,
+        18: -0.007777343749999998,
+        19: -0.007958984374999988,
+        20: -0.0077499999999999835,
+    }
+
     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.uuid == "301874d3-2e14-43c2-ba7f-e2b03ad05dde"
     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 loaded_agent._training_config.seed == 12345
     assert str(loaded_agent.session_path) == str(test_path)
 
+    # run another learn session
+    loaded_agent.learn()
+
+    learn_mean_rewards = av_rewards_dict(
+        loaded_agent.learning_path / f"average_reward_per_episode_{loaded_agent.timestamp_str}.csv"
+    )
+
+    # run is seeded so should have the expected learn value
+    assert learn_mean_rewards == expected_learn_mean_reward_per_episode
+
     # run an evaluation
     loaded_agent.evaluate()
 
@@ -63,38 +88,12 @@ def test_load_sb3_session():
     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
+    assert next(iter(set(eval_mean_reward.values()))) == -0.009896484374999988
 
     # delete the test directory
     shutil.rmtree(test_path)
 
 
-def test_load_rllib_session():
+def test_load_primaite_session():
-    """Test that loading an RLlib agent works."""
+    """Test that loading a Primaite session works."""
-    # test_path = copy_session_asset(TEST_ASSETS_ROOT)
+    pass
-    #
-    # 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)