def test_env_3(env3_robots):
env_data = env3_robots.get_env_metadata()
env3_robots.reset()
state = env3_robots.get_current_state()
action = [0, 0, 0]
action = n_from_prod(env_data['sets'], action)
new_state, reward, done, _ = env3_robots.step(action)
new_state_from_obs = get_state_from_observation(new_state)
assert done == False
assert reward == -0.666666
assert state.robots_data == new_state_from_obs.robots_data
assert state.time + 1 == new_state_from_obs.time
assert new_state_from_obs.positions == [2, 2, 3]
assert env3_robots.state.all() == np.array(new_state).all()
for i in range(3):
action = [0, 0, 0]
action = n_from_prod(env_data['sets'], action)
new_state, reward, done, _ = env3_robots.step(action)
action = [0, 0, 70]
action = n_from_prod(env_data['sets'], action)
new_state, reward, done, _ = env3_robots.step(action)
assert done == False
assert reward == 0.001
assert get_state_from_observation(new_state).time == 6
assert get_state_from_observation(new_state).robots_data == [15, 30, 0]
def test_one_minus_one_reward_good_env_4(env4_robots):
env_data = env4_robots.get_env_metadata()
action = [17, 0, 0, 0, 0, 13]
action = n_from_prod(env_data['sets'], action)
# {(r1 -> r0: 2), ,
# (r2 -> r3: 13)}
interpreted_action = env4_robots.get_action_from_space(action)
reward_giver = OneMinusOneRewardGiverAllowIllegal()
assert reward_giver.give_reward(State([10, 10, 15, 0], 10, None),
interpreted_action, env_data['meetings'],
env_data['cycle_lengths'],
env_data['max_memory']) == 0
assert reward_giver.give_reward(
State([10, 10, 10, 10], 10, None), interpreted_action,
env_data['meetings'], env_data['cycle_lengths'],
env_data['max_memory']) == settings.REWARD_FOR_INVALID_TRANSFER + 1
action = [25, 0, 0, 0, 0, 13]
action = n_from_prod(env_data['sets'], action)
# {(r1 -> r0: 10), ,
# (r2 -> r3: 13)}
interpreted_action = env4_robots.get_action_from_space(action)
assert reward_giver.give_reward(State([10, 10, 15, 0], 10, None),
interpreted_action, env_data['meetings'],
env_data['cycle_lengths'],
env_data['max_memory']) == 0
def test_one_minus_one_reward_bad_env_4(env4_robots):
env_data = env4_robots.get_env_metadata()
action = [1, 2, 20, 15, 13, 28]
action = n_from_prod(env_data['sets'], action)
# {(r0 -> r1: 1), (r0 -> r2: 2), (r3 -> r0: 5),
# (r1 -> r2: 15), (r1 -> r3: 13), (r3 -> r2: 13)}
interpreted_action = env4_robots.get_action_from_space(action)
reward_giver = OneMinusOneRewardGiverAllowIllegal()
assert reward_giver.give_reward(
State([10, 10, 10, 10], 8, None), interpreted_action,
env_data['meetings'], env_data['cycle_lengths'], env_data['max_memory']
) == 5 * settings.REWARD_FOR_INVALID_MEETING + settings.REWARD_FOR_INVALID_TRANSFER
assert reward_giver.give_reward(
State([0, 0, 0, 0], 8, None), interpreted_action, env_data['meetings'],
env_data['cycle_lengths'], env_data['max_memory']
) == 5 * settings.REWARD_FOR_INVALID_MEETING + settings.REWARD_FOR_INVALID_TRANSFER
action = [0] * (env_data['num_robots'] * (env_data['num_robots'] - 1) // 2)
action = n_from_prod(env_data['sets'], action)
interpreted_action = env4_robots.get_action_from_space(action)
assert reward_giver.give_reward(State([0, 0, 0, 0], 8, None),
interpreted_action, env_data['meetings'],
env_data['cycle_lengths'],
env_data['max_memory']) == 0
def test_apply_action_test_4_env(env4_robots):
env_data = env4_robots.get_env_metadata()
env4_robots.reset()
state = env4_robots.get_current_state()
state.time = 8
action = [19, 16, 0, 0, 0, 18]
action = n_from_prod(env_data['sets'], action)
interpreted_action = env4_robots.get_action_from_space(action)
new_state = apply_action_allow_illegal(state, interpreted_action,
env_data['max_memory'],
env_data['cycles'])
assert new_state == State(robots_data=[15, 6, 12, 7],
time=9,
positions=[2, 2, 5, 5])
action = [26, 16, 0, 0, 0, 22]
action = n_from_prod(env_data['sets'], action)
interpreted_action = env4_robots.get_action_from_space(action)
new_state = apply_action_allow_illegal(state, interpreted_action,
env_data['max_memory'],
env_data['cycles'])
assert new_state == State(robots_data=[11, 10, 9, 10],
time=9,
positions=[2, 2, 5, 5])
def step(self, action: int) -> Tuple[np.array, float, bool, Dict]:
# check if the action is in the action space
if not isinstance(action, list):
action = int(action)
else:
assert self.action_space.contains(
action), f'{action}, {type(action)} invalid'
action = n_from_prod(self.__sets, action)
interpreted_state = get_state_from_observation(
self.state) # convert observation to interpreted state
interpreted_action = self.get_action_from_space(action)
self.__state_action = interpreted_action
reward = self.__reward_class.give_reward(interpreted_state,
interpreted_action,
self.__meetings,
self.__cycles_lengths,
self.__max_memory)
if reward == settings.REWARD_FOR_INVALID_ACTION:
# new_state = interpreted_state
new_state = apply_action_only_increase_time_move_robots(
interpreted_state, interpreted_action, self.__max_memory,
self.__cycles)
else:
new_state = self.__action_apply(interpreted_state,
interpreted_action,
self.__max_memory, self.__cycles)
self.state = np.array(get_observation_from_state(new_state))
return self.state, reward / 1000000, check_if_done(
new_state, settings.MAXIMUM_NUM_ITER), {}
def test_one_minus_one_reward_bad_env_3(env3_robots):
env_data = env3_robots.get_env_metadata()
action = [15, 20, 56]
action = n_from_prod(env_data['sets'], action)
interpreted_action = env3_robots.get_action_from_space(action)
reward_giver = OneMinusOneRewardGiverAllowIllegal()
assert reward_giver.give_reward(
State([15, 20, 1], 5, None), interpreted_action, env_data['meetings'],
env_data['cycle_lengths'],
env_data['max_memory']) == 2 * settings.REWARD_FOR_INVALID_MEETING - 1
def test_apply_action_env_3_robots(env3_robots):
env_data = env3_robots.get_env_metadata()
env3_robots.reset()
state = env3_robots.get_current_state()
action = [61, 0, 0]
action = n_from_prod(env_data['sets'], action)
interpreted_action = env3_robots.get_action_from_space(action)
new_state = apply_action_allow_illegal(state, interpreted_action,
env_data['max_memory'],
env_data['cycles'])
assert new_state == State(robots_data=[21, 9, 15],
time=2,
positions=[2, 2, 3])
action = [61, 0, 0]
action = n_from_prod(env_data['sets'], action)
interpreted_action = env3_robots.get_action_from_space(action)
assert apply_action_allow_illegal(state, interpreted_action,
env_data['max_memory'],
env_data['cycles']) == new_state
def test_env_termination(env4_robots):
env_data = env4_robots.get_env_metadata()
env4_robots.reset()
action_map_terminate = {
'-1': [0] * 6,
'2': [25] + [0] * 5,
'4': [0, 25] + [0] * 4,
'6': [0] * 5 + [25],
'8': [0, 25] + [0] * 4
}
for i in range(1, 9):
if action_map_terminate.get(str(i), None):
action = action_map_terminate[str(i)]
action = n_from_prod(env_data['sets'], action)
_, __, done, ___ = env4_robots.step(action)
else:
action = action_map_terminate['-1']
action = n_from_prod(env_data['sets'], action)
_, __, done, ___ = env4_robots.step(action)
assert done == True
def test_one_minus_one_reward_good_env_3(env3_robots):
env_data = env3_robots.get_env_metadata()
action = [57, 0, 0]
action = n_from_prod(env_data['sets'], action)
interpreted_action = env3_robots.get_action_from_space(action)
reward_giver = OneMinusOneRewardGiverAllowIllegal()
assert reward_giver.give_reward(State([2, 2, 0], 10, None),
interpreted_action, env_data['meetings'],
env_data['cycle_lengths'],
env_data['max_memory']) == 1
action = [58, 0, 0]
action = n_from_prod(env_data['sets'], action)
interpreted_action = env3_robots.get_action_from_space(action)
assert reward_giver.give_reward(
State([0, 0, 0], 10, None), interpreted_action, env_data['meetings'],
env_data['cycle_lengths'],
env_data['max_memory']) == settings.REWARD_FOR_INVALID_TRANSFER