def run_offline_inference(agent: Trainer, env: CrossAdaptiveEnv):
# NOTE: something is wrong here. For some reason, all the action values are too close to the bound
done = False
obs = env.reset()
while not done:
action = agent.compute_action(obs)
# TODO: standardize action
# it might be difficult to standardize the action in live mode, but offline inference essentially work
obs, _, done, _ = env.step(action)
def run_live_inference(
agent: Trainer,
env: CrossAdaptiveEnv,
):
mediator = Mediator()
episode_index = 0
while episode_index < 1500:
source_features, target_features = mediator.get_features()
if source_features is None or target_features is None:
continue
else:
# trim off timestamp
source_features = source_features[1:]
target_features = target_features[1:]
standardized_source = np.array([
env.standardizer.get_standardized_value(
env.analyser.analysis_features[i], feature_value)
for i, feature_value in enumerate(source_features)
])
standardized_target = np.array([
env.standardizer.get_standardized_value(
env.analyser.analysis_features[i], feature_value)
for i, feature_value in enumerate(target_features)
])
obs = np.concatenate((standardized_source, standardized_target))
print(np.round(obs, decimals=2))
action = agent.compute_action(obs)
# action = env.action_space.sample()
mapping = env.action_to_mapping(action)
# print(mapping)
mediator.send_effect_mapping(mapping)
episode_index += 1
mediator.terminate()
print("\n\n\tDONE\n\n")