75 lines
2.8 KiB
Python
75 lines
2.8 KiB
Python
from datetime import time, datetime
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from primaite.environment.primaite_env import Primaite
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from tests import TEST_CONFIG_ROOT
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from tests.conftest import _get_temp_session_path
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def run_generic(env, config_values):
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"""Run against a generic agent."""
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# Reset the environment at the start of the episode
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env.reset()
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for episode in range(0, config_values.num_episodes):
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for step in range(0, config_values.num_steps):
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# Send the observation space to the agent to get an action
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# TEMP - random action for now
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# action = env.blue_agent_action(obs)
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# action = env.action_space.sample()
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action = 0
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# Run the simulation step on the live environment
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obs, reward, done, info = env.step(action)
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# Break if done is True
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if done:
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break
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# Introduce a delay between steps
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time.sleep(config_values.time_delay / 1000)
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# Reset the environment at the end of the episode
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env.reset()
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env.close()
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def test_random_red_agent_behaviour():
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"""
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Test that hardware state is penalised at each step.
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When the initial state is OFF compared to reference state which is ON.
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"""
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list_of_node_instructions = []
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for i in range(2):
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"""Takes a config path and returns the created instance of Primaite."""
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session_timestamp: datetime = datetime.now()
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session_path = _get_temp_session_path(session_timestamp)
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timestamp_str = session_timestamp.strftime("%Y-%m-%d_%H-%M-%S")
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env = Primaite(
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training_config_path=TEST_CONFIG_ROOT / "one_node_states_on_off_main_config.yaml",
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lay_down_config_path=TEST_CONFIG_ROOT / "one_node_states_on_off_lay_down_config.yaml",
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transaction_list=[],
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session_path=session_path,
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timestamp_str=timestamp_str,
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)
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training_config = env.training_config
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training_config.num_steps = env.episode_steps
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# TOOD: This needs t be refactored to happen outside. Should be part of
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# a main Session class.
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if training_config.agent_identifier == "GENERIC":
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run_generic(env, training_config)
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all_red_actions = env.red_node_pol
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list_of_node_instructions.append(all_red_actions)
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# assert not (list_of_node_instructions[0].__eq__(list_of_node_instructions[1]))
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print(list_of_node_instructions[0]["1"].get_start_step())
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print(list_of_node_instructions[0]["1"].get_end_step())
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print(list_of_node_instructions[0]["1"].get_target_node_id())
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print(list_of_node_instructions[1]["1"].get_start_step())
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print(list_of_node_instructions[1]["1"].get_end_step())
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print(list_of_node_instructions[1]["1"].get_target_node_id())
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assert list_of_node_instructions[0].__ne__(list_of_node_instructions[1])
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