273876873e
- renamed _config_values_main to training_config.py and renamed the ConfigValuesMain class to TrainingConfig. Moved training_config.py to src/primaite/config/training_config.py - Renamed all training config yaml file keys to make creating an instance of TrainingConfig easier. Moved action_type and num_steps over to the training config. - Decoupled the training config and lay down config. - Refactored main.py so that it can be ran from CLI and can take a training config path and a lay down config path. - refactored all outputs so that they save to the session dir. - Added some necessary setup scripts that handle creating app dirs, fronting example config files to the user, fronting demo notebooks to the user, performing clean-up in between installations etc. - Added functions that attempt to retrieve the file path of users example config files that have been fronted by the primaite setup. - Added logging config and a getLogger function in the top-level init. - Refactored all logs entries logged to use a logger using the primaite logging config. - Added basic typer CLI for doing things like setup, viewing logs, viewing primaite version, running a basic session. - Updated test to use new features and config structures. - Began updating docs. More to do here.
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
# Crown Copyright (C) Dstl 2022. DEFCON 703. Shared in confidence.
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import time
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from pathlib import Path
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from typing import Union
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from primaite.environment.primaite_env import Primaite
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ACTION_SPACE_NODE_VALUES = 1
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ACTION_SPACE_NODE_ACTION_VALUES = 1
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def _get_primaite_env_from_config(
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training_config_path: Union[str, Path], lay_down_config_path: Union[str, Path]
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):
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"""Takes a config path and returns the created instance of Primaite."""
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env = Primaite(
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training_config_path=training_config_path,
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lay_down_config_path=lay_down_config_path,
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transaction_list=[],
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)
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config_values = env.config_values
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config_values.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 env.config_values.agent_identifier == "GENERIC":
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run_generic(env, config_values)
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return env
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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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# 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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