def test_calibrated_agent(self):
"""Does the calibrated agent work as expected?"""
# Initialize engine
eng = engines.ContinuousTwoZoneEngine(
nsteps=100,
use_qdot=True,
fuel="PRF100",
rxnmech="llnl_gasoline_surrogate_323.xml",
ename="Isooctane_MBT_DI_50C_Summ.xlsx",
)
# Initialize the agent
env = DummyVecEnv([lambda: eng])
agent = agents.CalibratedAgent(env)
agent.learn()
# Evaluate the agent
t0 = time.time()
df, total_reward = utilities.evaluate_agent(env, agent)
elapsed = time.time() - t0
utilities.plot_df(env, df, idx=0, name="calibrated")
# Test
npt.assert_allclose(np.linalg.norm(df.V), 0.002195212151)
npt.assert_allclose(np.linalg.norm(df.p), 22012100.17143623)
npt.assert_allclose(np.linalg.norm(df["T"]), 14210.47662980)
npt.assert_allclose(np.linalg.norm(df.rewards), 104.47362155)
npt.assert_allclose(np.linalg.norm(df.mdot), 0.04144044)
npt.assert_allclose(np.linalg.norm(df.qdot), 97686.91574242)
print(f"Wall time for CalibratedAgent = {elapsed} seconds")
def generate_expert_traj(self, fname):
df, total_reward = utilities.evaluate_agent(self.env, self)
episode_starts = [False for _ in range(len(df))]
episode_starts[-1] = True
numpy_dict = {
"actions": df[self.eng.action.actions].values,
"obs": df[self.env.get_attr("observables", indices=0)[0]].values,
"rewards": df.rewards.values,
"episode_returns": np.array([total_reward]),
"episode_starts": episode_starts,
}
np.savez(fname, **numpy_dict)
return numpy_dict
def test_exhaustive_agent(self):
"""Does the exhaustive agent work as expected?"""
# Initialize engine
eng = engines.DiscreteTwoZoneEngine(
nsteps=101,
mdot=0.1,
max_minj=2.5e-5,
fuel="dodecane",
rxnmech="dodecane_lu_nox.cti",
ename="Isooctane_MBT_DI_50C_Summ.xlsx",
reward=rw.Reward(negative_reward=-0.05),
)
env = DummyVecEnv([lambda: eng])
variables = eng.observables + eng.internals + eng.histories
df = pd.DataFrame(
columns=list(dict.fromkeys(variables + eng.action.actions + ["rewards"]))
)
# Initialize the agent
agent = agents.ExhaustiveAgent(env)
agent.learn()
# Evaluate the agent
t0 = time.time()
df, total_reward = utilities.evaluate_agent(env, agent)
elapsed = time.time() - t0
utilities.plot_df(env, df, idx=1, name="exhaustive")
# Test
npt.assert_allclose(np.linalg.norm(df.V), 0.002205916821815495)
npt.assert_allclose(np.linalg.norm(df.p), 25431213.9403193)
npt.assert_allclose(np.linalg.norm(df["T"]), 13611.58370927)
npt.assert_allclose(np.linalg.norm(df.rewards), 101.41373957)
npt.assert_allclose(np.linalg.norm(df.mdot), 0.1)
print(f"Wall time for ExhaustiveAgent = {elapsed} seconds")
tensorboard_log=logdir,
)
agent.learn(
total_timesteps=agent_params["number_episodes"].value *
(eng_params["nsteps"].value - 1) * agent_params["nranks"].value,
callback=callback,
)
elif agent_params["agent"].value == "manual":
env = DummyVecEnv([lambda: eng])
agent = agents.ManualAgent(env)
agent.learn(agent_params["injection_cas"].value,
agent_params["qdot_cas"].value)
# Save, evaluate, and plot the agent
pfx = os.path.join(logdir, "agent")
agent.save(pfx)
env = DummyVecEnv([lambda: eng])
df, total_reward = utilities.evaluate_agent(env, agent)
df.to_csv(pfx + ".csv", index=False)
utilities.plot_df(env, df, idx=0, name=agent_params["agent"].value)
utilities.save_plots(pfx + ".pdf")
# Plot the training history
logs = pd.read_csv(logname)
utilities.plot_training(logs, os.path.join(logdir, "logger.pdf"))
# output timer
end = time.time() - start
print(f"Elapsed time {timedelta(seconds=end)} (or {end} seconds)")