def test_iterate_value_q_pi_multi_threaded():
thread_manager = RunThreadManager(True)
def train_thread_target():
random_state = RandomState(12345)
mdp_environment: Gridworld = Gridworld.example_4_1(random_state, None)
q_S_A = TabularStateActionValueEstimator(mdp_environment, 0.1, None)
mdp_agent = StochasticMdpAgent('test', random_state,
q_S_A.get_initial_policy(), 1)
iterate_value_q_pi(agent=mdp_agent,
environment=mdp_environment,
num_improvements=1000000,
num_episodes_per_improvement=10,
num_updates_per_improvement=None,
alpha=0.1,
mode=Mode.SARSA,
n_steps=None,
planning_environment=None,
make_final_policy_greedy=False,
q_S_A=q_S_A,
thread_manager=thread_manager,
num_improvements_per_plot=10)
# premature update should have no effect
assert update_policy_iteration_plot() is None
# initialize plot from main thread
plot_policy_iteration(iteration_average_reward=[],
iteration_total_states=[],
iteration_num_states_improved=[],
elapsed_seconds_average_rewards={},
pdf=None)
# run training thread
run_thread = Thread(target=train_thread_target)
run_thread.start()
time.sleep(1)
# update plot asynchronously
update_policy_iteration_plot()
time.sleep(1)
# should be allowed to update plot from non-main thread
def bad_update():
with pytest.raises(ValueError,
match='Can only update plot on main thread.'):
update_policy_iteration_plot()
bad_thread = Thread(target=bad_update)
bad_thread.start()
bad_thread.join()
thread_manager.abort = True
run_thread.join()
def bad_update():
with pytest.raises(ValueError,
match='Can only update plot on main thread.'):
update_policy_iteration_plot()