Full algorithm and ae tests running now

tests/test_ae.py

@@ -1,5 +1,4 @@
 import inspect
-import os
 
 import pytest
 import torch
@@ -8,32 +7,7 @@
 from cares_reinforcement_learning.encoders.autoencoder_factory import AEFactory
 from cares_reinforcement_learning.encoders.configurations import AEConfig, BurgessConfig
 
-IN_GITHUB_ACTIONS = os.getenv("GITHUB_ACTIONS") == "true"
 
-
-@pytest.mark.skipif(not IN_GITHUB_ACTIONS, reason="Running more complex test locally")
-def test_ae_factory():
-    factory = AEFactory()
-
-    ae_configurations = {}
-    for name, cls in inspect.getmembers(configurations, inspect.isclass):
-        if issubclass(cls, AEConfig) and cls != AEConfig and cls != BurgessConfig:
-            name = name.replace("Config", "")
-            ae_configurations[name] = cls
-
-    for ae, config in ae_configurations.items():
-
-        observation_size = (9, 84, 84)
-
-        config = config(latent_dim=100)
-
-        ae = factory.create_autoencoder(
-            observation_size=observation_size, config=config
-        )
-        assert ae is not None, f"{ae} was not created successfully"
-
-
-@pytest.mark.skipif(IN_GITHUB_ACTIONS, reason="Test doesn't work in Github Actions.")
 def test_ae():
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 

tests/test_algorithms.py

@@ -1,5 +1,4 @@
 import inspect
-import os
 from random import randrange
 
 import numpy as np
@@ -9,28 +8,6 @@
 from cares_reinforcement_learning.util import NetworkFactory, configurations
 from cares_reinforcement_learning.util.configurations import AlgorithmConfig
 
-# IN_GITHUB_ACTIONS = os.getenv("GITHUB_ACTIONS") == "true"
-
-
-# @pytest.mark.skipif(not IN_GITHUB_ACTIONS, reason="Running more complex test locally")
-# def test_network_factory():
-#     factory = NetworkFactory()
-
-#     algorithm_configurations = {}
-#     for name, cls in inspect.getmembers(configurations, inspect.isclass):
-#         if issubclass(cls, AlgorithmConfig) and cls != AlgorithmConfig:
-#             name = name.replace("Config", "")
-#             algorithm_configurations[name] = cls
-
-#     for algorithm, config in algorithm_configurations.items():
-#         config = config()
-#         observation_size = (9, 84, 84) if config.image_observation else 10
-#         action_num = 2
-#         network = factory.create_network(
-#             observation_size=observation_size, action_num=action_num, config=config
-#         )
-#         assert network is not None, f"{algorithm} was not created successfully"
-
 
 def _policy_buffer(
     memory_buffer,