#1595:

- Added ability to load sessions via PrimaiteSession
- PrimaiteSession loading test
- Added a NotImplemented RLlib loading for now
- Added the ability to load sessions for hardcoded agents
- Moved Session metadata parsing to utils

tests/test_session_loading.py

@@ -8,6 +8,7 @@ from uuid import uuid4
 from primaite import getLogger
 from primaite.agents.sb3 import SB3Agent
 from primaite.common.enums import AgentFramework, AgentIdentifier
+from primaite.primaite_session import PrimaiteSession
 from primaite.utils.session_output_reader import av_rewards_dict
 from tests import TEST_ASSETS_ROOT
 
@@ -96,4 +97,59 @@ def test_load_sb3_session():
 
 def test_load_primaite_session():
     """Test that loading a Primaite session works."""
-    pass
+    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")
+
+    # create loaded session
+    session = PrimaiteSession(session_path=test_path)
+
+    # run setup on session
+    session.setup()
+
+    # make sure that the session was loaded correctly
+    assert session._agent_session.uuid == "301874d3-2e14-43c2-ba7f-e2b03ad05dde"
+    assert session._agent_session._training_config.agent_framework == AgentFramework.SB3.name
+    assert session._agent_session._training_config.agent_identifier == AgentIdentifier.PPO.name
+    assert session._agent_session._training_config.deterministic
+    assert session._agent_session._training_config.seed == 12345
+    assert str(session._agent_session.session_path) == str(test_path)
+
+    # run another learn session
+    session.learn()
+
+    learn_mean_rewards = av_rewards_dict(
+        session.learning_path / f"average_reward_per_episode_{session.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
+    session.evaluate()
+
+    # load the evaluation average reward csv file
+    eval_mean_reward = av_rewards_dict(
+        session.evaluation_path / f"average_reward_per_episode_{session.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()))) == -0.009896484374999988
+
+    # delete the test directory
+    shutil.rmtree(test_path)