def test_2_busswitch(self):
agent = TopologyGreedy(self.env.helper_action_player)
i, cum_reward = self._aux_test_agent(agent, i_max=10)
assert i == 11, "The powerflow diverged before step 10 for greedy agent"
assert np.abs(
cum_reward - 12075.38800
) <= self.tol_one, "The reward has not been properly computed"
def test_2_busswitch(self):
agent = TopologyGreedy(self.env.helper_action_player)
with warnings.catch_warnings():
warnings.filterwarnings("error")
i, cum_reward = self._aux_test_agent(agent, i_max=10)
assert i == 11, "The powerflow diverged before step 10 for greedy agent"
expected_reward = dt_float(12075.389)
assert np.abs(cum_reward - expected_reward) <= self.tol_one, "The reward has not been properly computed"
def main(max_ts, name):
backend = LightSimBackend()
param = Parameters()
param.init_from_dict({"NO_OVERFLOW_DISCONNECTION": True})
env_klu = make(name,
backend=backend,
param=param,
gamerules_class=AlwaysLegal,
test=True)
agent = TopologyGreedy(action_space=env_klu.action_space)
nb_ts_klu, time_klu, aor_klu, gen_p_klu, gen_q_klu = run_env(
env_klu, max_ts, agent)
env_pp = make(name, param=param, gamerules_class=AlwaysLegal, test=True)
agent = TopologyGreedy(action_space=env_pp.action_space)
nb_ts_pp, time_pp, aor_pp, gen_p_pp, gen_q_pp = run_env(
env_pp, max_ts, agent)
print_res(env_klu, env_pp, nb_ts_klu, nb_ts_pp, time_klu, time_pp, aor_klu,
aor_pp, gen_p_klu, gen_p_pp, gen_q_klu, gen_q_pp)
def test_2_busswitch(self):
agent = TopologyGreedy(self.env.action_space)
with warnings.catch_warnings():
warnings.filterwarnings("error")
i, cum_reward, all_acts = self._aux_test_agent(agent, i_max=10)
assert i == 11, "The powerflow diverged before step 10 for greedy agent"
expected_reward = dt_float(12075.389) # i have more actions now, so this is not correct (though it should be..
# yet a proof that https://github.com/rte-france/Grid2Op/issues/86 is grounded
expected_reward = dt_float(12277.632)
# 12076.356
# 12076.191
expected_reward = dt_float(12076.356)
assert np.abs(cum_reward - expected_reward) <= self.tol_one, "The reward has not been properly computed"
print("do-nothing")
with make(dataset, param=params, backend=backend) as env:
agent = DoNothingAgent(env.action_space)
runner = Runner(**env.get_params_for_runner(),
agentClass=None,
agentInstance=agent)
runner.run(
nb_episode=2,
path_save="grid2viz/data/agents/do-nothing-baseline",
nb_process=1,
max_iter=2000,
pbar=True,
)
env.close()
print("greedy")
with make(dataset, param=params, backend=backend) as env:
agent = TopologyGreedy(env.action_space)
runner = Runner(**env.get_params_for_runner(),
agentClass=None,
agentInstance=agent)
runner.run(
nb_episode=2,
path_save="grid2viz/data/agents/greedy-baseline",
nb_process=1,
max_iter=2000,
pbar=True,
)
env.close()