Merge remote-tracking branch 'origin/dev' into feature/917_Integrate_with_RLLib

# Conflicts:
#	src/primaite/config/_package_data/training/training_config_main.yaml
#	src/primaite/environment/primaite_env.py

tests/test_red_random_agent_behaviour.py

@@ -0,0 +1,77 @@
+from datetime import datetime
+
+from primaite.config.lay_down_config import data_manipulation_config_path
+from primaite.environment.primaite_env import Primaite
+from primaite.nodes.node_state_instruction_red import NodeStateInstructionRed
+from tests import TEST_CONFIG_ROOT
+from tests.conftest import _get_temp_session_path
+
+
+def run_generic(env, config_values):
+    """Run against a generic agent."""
+    # Reset the environment at the start of the episode
+    env.reset()
+    for episode in range(0, config_values.num_episodes):
+        for step in range(0, config_values.num_steps):
+            # Send the observation space to the agent to get an action
+            # TEMP - random action for now
+            # action = env.blue_agent_action(obs)
+            # action = env.action_space.sample()
+            action = 0
+
+            # Run the simulation step on the live environment
+            obs, reward, done, info = env.step(action)
+
+            # Break if done is True
+            if done:
+                break
+
+        # Reset the environment at the end of the episode
+        env.reset()
+
+    env.close()
+
+
+def test_random_red_agent_behaviour():
+    """
+    Test that hardware state is penalised at each step.
+
+    When the initial state is OFF compared to reference state which is ON.
+    """
+    list_of_node_instructions = []
+
+    # RUN TWICE so we can make sure that red agent is randomised
+    for i in range(2):
+        """Takes a config path and returns the created instance of Primaite."""
+        session_timestamp: datetime = datetime.now()
+        session_path = _get_temp_session_path(session_timestamp)
+
+        timestamp_str = session_timestamp.strftime("%Y-%m-%d_%H-%M-%S")
+        env = Primaite(
+            training_config_path=TEST_CONFIG_ROOT
+            / "one_node_states_on_off_main_config.yaml",
+            lay_down_config_path=data_manipulation_config_path(),
+            transaction_list=[],
+            session_path=session_path,
+            timestamp_str=timestamp_str,
+        )
+        # set red_agent_
+        env.training_config.random_red_agent = True
+        training_config = env.training_config
+        training_config.num_steps = env.episode_steps
+
+        run_generic(env, training_config)
+        # add red pol instructions to list
+        list_of_node_instructions.append(env.red_node_pol)
+
+    # compare instructions to make sure that red instructions are truly random
+    for index, instruction in enumerate(list_of_node_instructions):
+        for key in list_of_node_instructions[index].keys():
+            instruction: NodeStateInstructionRed = list_of_node_instructions[index][key]
+            print(f"run {index}")
+            print(f"{key} start step: {instruction.get_start_step()}")
+            print(f"{key} end step: {instruction.get_end_step()}")
+            print(f"{key} target node id: {instruction.get_target_node_id()}")
+            print("")
+
+    assert list_of_node_instructions[0].__ne__(list_of_node_instructions[1])