def run_single_classifier_experiment(strageties, expt_name, dataset_name,
run_name):
for strat in strageties:
agent_config[strat] = STRATS[strat]
env_config = {
'env_id': 'Flex-v1',
'dataset_name': dataset_name,
'episode_length': 0,
'flex_size': 0.02,
'max_flex_time': 6,
'relax_time': 0
}
expt_path = os.path.join(os.getcwd(), 'results', expt_name)
paths = energy_py.make_paths(expt_path, run_name=run_name)
logger = energy_py.make_logger(paths, 'master')
energy_py.experiment(agent_config=agent_config,
env_config=env_config,
total_steps=total_steps,
paths=paths,
seed=args.seed)
def test_cartpole_expt():
env_config = {'env_id': 'cartpole-v0'}
experiment(agent_config=AGENT_CONFIG,
env_config=env_config,
total_steps=TOTAL_STEPS,
paths=PATHS)
def test_battery_expt():
env_config = {'env_id': 'battery',
'dataset': 'example',
'episode_length': 10,
'episode_sample': 'random',
'initial_charge': 'random'}
experiment(agent_config=AGENT_CONFIG,
env_config=env_config,
total_steps=TOTAL_STEPS,
paths=PATHS)
def test_cartpole_expt():
env = CartPoleEnv()
agent, env, sess = experiment(agent=DQN,
agent_config=AGENT_CONFIG,
env=env,
total_steps=TOTAL_STEPS,
data_path=DATA_PATH,
results_path=RESULTS_PATH)
def test_flex_expt():
env = FlexEnv
env_config = {'episode_length': 10, 'episode_random': True}
agent, env, sess = experiment(agent=DQN,
agent_config=AGENT_CONFIG,
env=env,
env_config=env_config,
total_steps=TOTAL_STEPS,
data_path=DATA_PATH,
results_path=RESULTS_PATH)
def test_battery_expt():
env = BatteryEnv
env_config = {
'episode_length': 10,
'episode_random': True,
'initial_charge': 'random'
}
agent, env, sess = experiment(agent=DQN,
agent_config=AGENT_CONFIG,
env=env,
env_config=env_config,
total_steps=TOTAL_STEPS,
data_path=DATA_PATH,
results_path=RESULTS_PATH)
import logging
import os
from energy_py import experiment
from energy_py.agents import ClassifierAgent
from energy_py.envs import FlexEnv
if __name__ == '__main__':
total_steps = 1e6
data_path = os.getcwd() + '/classifier_deterministic/'
results_path = os.getcwd() + '/results/classifier/'
env_config = {
'episode_length': 0,
'episode_random': False,
'data_path': data_path,
'flex_effy': 1.0,
'flex_size': 0.05
}
agent_config = {'discount': 0.9}
total_steps = 10
env = FlexEnv
agent = ClassifierAgent
agent, env, sess = experiment(agent, agent_config, env, total_steps,
data_path, results_path, env_config)
total_steps = 5e5
agent = DQN
agent_config = {
'discount': 0.97,
'tau': 0.001,
'batch_size': 32,
'layers': (10, 10, 10),
'learning_rate': 0.0001,
'epsilon_decay_fraction': 0.3,
'memory_fraction': 0.1,
'memory_type': 'deque',
'double_q': True,
'total_steps': total_steps,
'target_processor': 'normalizer',
'observation_processor': 'standardizer'
}
env = CartPoleEnv()
data_path = os.getcwd() + '/gym/'
results_path = os.getcwd() + '/results/cartpole/'
agent, env, sess = experiment(agent=DQN,
agent_config=agent_config,
env=env,
total_steps=total_steps,
data_path=data_path,
results_path=results_path,
run_name='DDQN')
env_config = {'env_id': 'flex-v0',
'dataset': 'tempus',
'flex_size': 0.5,
'flex_time': 1,
'relax_time': 0,
'episode_length': 2016,
'episode_sample': 'random'}
# env_config = {'env_id': 'CartPole'}
# env_config = {'env_id': 'Battery',
# 'dataset_name': args.dataset,
# 'episode_sample': 'random'}
# env_config = {'env_id': 'Flex-v0',
# 'dataset_name': args.dataset,
# 'episode_sample': 'random'}
expt_path = os.path.join(os.getcwd(),
'results',
args.expt_name)
paths = make_paths(expt_path, run_name=args.run_name)
logger = make_logger(paths, 'master')
experiment(agent_config=agent_config,
env_config=env_config,
total_steps=TOTAL_STEPS,
paths=paths,
seed=args.seed)