def test_policy_crossing_for_non_continuous_agent_range(self):
"""
* Solve independently for agent groups [[1], [0,2]]
* Cross the policies
* Make sure the crossed policy behaves right
"""
env = create_mapf_env('room-32-32-4', 15, 3, 0, 0, -1000, 0, -1)
interesting_locations = ((19, 22), (18, 24), (17, 22))
plan_func = partial(
fixed_iterations_count_rtdp,
partial(local_views_prioritized_value_iteration_min_heuristic,
1.0), 1.0, 100)
crossed_policy = solve_independently_and_cross(env, [[1], [0, 2]],
plan_func, {})
policy0 = plan_func(get_local_view(env, [1]), {})
policy1 = plan_func(get_local_view(env, [0, 2]), {})
action0 = policy0.act(
policy0.env.locations_to_state((interesting_locations[1], )))
action1 = policy1.act(
policy1.env.locations_to_state((interesting_locations[0], ) +
(interesting_locations[2], )))
vector_action0 = integer_action_to_vector(action0, 1)
vector_action1 = integer_action_to_vector(action1, 2)
vector_action_local = (vector_action1[0], vector_action0[0],
vector_action1[1])
joint_action = crossed_policy.act(
env.locations_to_state(interesting_locations))
vector_action_joint = integer_action_to_vector(joint_action, 3)
self.assertEqual(vector_action_local, vector_action_joint)
def test_conflict_detected_for_room_scenario_with_crossed_policy(self):
env = create_mapf_env('room-32-32-4', 1, 2, 0.1, 0.1, -1000, 0, -1)
policy1 = fixed_iterations_count_rtdp(
partial(local_views_prioritized_value_iteration_min_heuristic,
1.0), 1.0, 100, get_local_view(env, [0]), {})
policy2 = fixed_iterations_count_rtdp(
partial(local_views_prioritized_value_iteration_min_heuristic,
1.0), 1.0, 100, get_local_view(env, [1]), {})
crossed_policy = CrossedPolicy(env, [policy1, policy2], [[0], [1]])
self.assertIsNot(detect_conflict(env, crossed_policy), None)
def test_dijkstra_sum_sanity_room_env_large_goal_reward(self):
env = create_mapf_env('sanity-2-8', None, 2, 0, 0, -1000, 100, -1)
env0 = get_local_view(env, [0])
env1 = get_local_view(env, [1])
dijkstra_func = dijkstra_sum_heuristic(env)
vi_policy0 = prioritized_value_iteration(1.0, env0, {})
vi_policy1 = prioritized_value_iteration(1.0, env1, {})
for s in range(env.nS):
s0 = env0.locations_to_state((env.state_to_locations(s)[0], ))
s1 = env0.locations_to_state((env.state_to_locations(s)[1], ))
self.assertEqual(dijkstra_func(s),
vi_policy0.v[s0] + vi_policy1.v[s1])
def __init__(self, env, gamma, heuristic):
super(MultiagentRtdpPolicy, self).__init__(env, gamma, heuristic)
self.q_partial_table = {
i: defaultdict(dict)
for i in range(env.n_agents)
}
self.local_env_aux = get_local_view(self.env, [0])
def test_detect_conflict_detects_switching(self):
"""
* Create an env which its independent optimal policies cause a SWITCHING conflict
* Solve independently
* Make sure the conflict is detected
"""
env = create_mapf_env('room-32-32-4', 9, 2, 0, 0, -1000, 0, -1)
low_level_plan_func = partial(
fixed_iterations_count_rtdp,
partial(local_views_prioritized_value_iteration_min_heuristic,
1.0), 1.0, 100)
policy = solve_independently_and_cross(env, [[0], [1]],
low_level_plan_func, {})
conflict = detect_conflict(env, policy)
# Assert a conflict detected
self.assertIsNotNone(conflict)
aux_local_env = get_local_view(env, [0])
agent_1_state = aux_local_env.locations_to_state(((21, 20), ))
agent_0_state = aux_local_env.locations_to_state(((21, 19), ))
possible_conflicts = [((1, agent_1_state, agent_0_state),
(0, agent_0_state, agent_1_state)),
((0, agent_0_state, agent_1_state),
(1, agent_1_state, agent_0_state))]
# Assert the conflict parameters are right
self.assertIn(conflict, possible_conflicts)
def test_detect_conflict_finds_classical_conflict(self):
grid = MapfGrid(['...', '@.@', '...'])
agents_starts = ((0, 0), (0, 2))
agents_goals = ((2, 0), (2, 2))
env = MapfEnv(grid, 2, agents_starts, agents_goals, 0, 0, -1, 1, -0.01)
policy1 = {
0: ACTIONS.index(RIGHT),
1: ACTIONS.index(STAY),
2: ACTIONS.index(DOWN),
3: ACTIONS.index(DOWN),
4: ACTIONS.index(LEFT),
5: ACTIONS.index(RIGHT),
6: ACTIONS.index(LEFT),
}
policy2 = {
0: ACTIONS.index(RIGHT),
1: ACTIONS.index(RIGHT),
2: ACTIONS.index(DOWN),
3: ACTIONS.index(DOWN),
4: ACTIONS.index(RIGHT),
5: ACTIONS.index(LEFT),
6: ACTIONS.index(STAY),
}
joint_policy = CrossedPolicy(env, [
DictPolicy(get_local_view(env, [0]), 1.0, policy1),
DictPolicy(get_local_view(env, [1]), 1.0, policy2)
], [[0], [1]])
aux_local_env = get_local_view(env, [0])
self.assertEqual(
detect_conflict(env, joint_policy),
((0, aux_local_env.locations_to_state(
((0, 0), )), aux_local_env.locations_to_state(((0, 1), ))),
(1, aux_local_env.locations_to_state(
((0, 2), )), aux_local_env.locations_to_state(((0, 1), )))))
def test_detect_conflict_return_none_when_no_conflict(self):
grid = MapfGrid(['...', '...', '...'])
agents_starts = ((0, 0), (0, 2))
agents_goals = ((2, 0), (2, 2))
env = MapfEnv(grid, 2, agents_starts, agents_goals, 0, 0, -1, 1, -0.01)
policy1 = {
0: ACTIONS.index(DOWN),
1: ACTIONS.index(DOWN),
2: ACTIONS.index(DOWN),
3: ACTIONS.index(DOWN),
4: ACTIONS.index(DOWN),
5: ACTIONS.index(DOWN),
6: ACTIONS.index(DOWN),
7: ACTIONS.index(DOWN),
8: ACTIONS.index(DOWN),
}
policy2 = {
0: ACTIONS.index(DOWN),
1: ACTIONS.index(DOWN),
2: ACTIONS.index(DOWN),
3: ACTIONS.index(DOWN),
4: ACTIONS.index(DOWN),
5: ACTIONS.index(DOWN),
6: ACTIONS.index(DOWN),
7: ACTIONS.index(DOWN),
8: ACTIONS.index(DOWN),
}
joint_policy = CrossedPolicy(env, [
DictPolicy(get_local_view(env, [0]), 1.0, policy1),
DictPolicy(get_local_view(env, [1]), 1.0, policy2)
], [[0], [1]])
self.assertEqual(detect_conflict(env, joint_policy), None)
def insert_scenario_metadata(log_func, insert_to_db_func,
scenario_metadata: ScenarioMetadata):
scen_data = {
'type': 'scenario_data',
'map': scenario_metadata.map,
'scen_id': scenario_metadata.scen_id,
'fail_prob': scenario_metadata.fail_prob,
'n_agents': scenario_metadata.n_agents,
}
configuration_string = '_'.join(
[f'{key}:{value}' for key, value in scen_data.items()])
scen_data['valid'] = True
log_func(DEBUG, f'starting scenario data for {configuration_string}')
log_func(
DEBUG,
f'starting solving independent agents for {configuration_string}')
try:
env = create_mapf_env(scenario_metadata.map, scenario_metadata.scen_id,
scenario_metadata.n_agents,
scenario_metadata.fail_prob / 2,
scenario_metadata.fail_prob / 2, -1000, -1, -1)
except KeyError:
log_func(ERROR, f'{configuration_string} is invalid')
scen_data['valid'] = False
insert_to_db_func(scen_data)
return
# Calculate single agent rewards
scen_data['self_agent_reward'] = []
for i in range(env.n_agents):
pvi_plan_func = partial(prioritized_value_iteration, 1.0)
local_env = get_local_view(env, [i])
policy = pvi_plan_func(local_env, {})
local_env.reset()
self_agent_reward = float(policy.v[local_env.s])
scen_data['self_agent_reward'].append(self_agent_reward)
log_func(DEBUG,
f'inserting scenario data for {configuration_string} to DB')
# Insert stats about this instance to the DB
insert_to_db_func(scen_data)
def test_couple_detect_conflict_3_agents_multiple_agents_in_group(self):
"""This test may sometime be used to test detecting a conflict for only a couple of agents.
The test will make sure that agent 0 got no conflicts with 1 and 2 while agents 1 and 2 do get a conflict.
Now agent 1 will be a part of a group contains both agent 0 and 1 ([0,1]). This way agent 1 index in its
group will be 1 and not 0. This case is catching a bug I had previously.
"""
grid = MapfGrid(['...', '...', '...'])
agents_starts = ((0, 0), (2, 0), (2, 2))
agents_goals = ((0, 2), (2, 2), (2, 0))
env = MapfEnv(grid, 3, agents_starts, agents_goals, 0, 0, -1, 1, -0.01)
single_agent_env = MapfEnv(grid, 1, (agents_starts[0], ),
(agents_goals[0], ), 0, 0, -1, 1, -0.01)
env01 = get_local_view(env, [0, 1])
# >>S
# SSS
# SSS
policy0 = {
0: ACTIONS.index(RIGHT),
1: ACTIONS.index(STAY),
2: ACTIONS.index(STAY),
3: ACTIONS.index(RIGHT),
4: ACTIONS.index(STAY),
5: ACTIONS.index(STAY),
6: ACTIONS.index(STAY),
7: ACTIONS.index(STAY),
8: ACTIONS.index(STAY),
}
# SSS
# SSS
# >>S
policy1 = {
0: ACTIONS.index(STAY),
1: ACTIONS.index(STAY),
2: ACTIONS.index(RIGHT),
3: ACTIONS.index(STAY),
4: ACTIONS.index(STAY),
5: ACTIONS.index(RIGHT),
6: ACTIONS.index(STAY),
7: ACTIONS.index(STAY),
8: ACTIONS.index(STAY),
}
# policy01 is a cross between agent 0 and agent 1
policy01 = {}
for s0 in range(9):
for s1 in range(9):
joint_state = env01.locations_to_state(
(single_agent_env.state_to_locations(s0)[0],
single_agent_env.state_to_locations(s1)[0]))
policy01[joint_state] = vector_action_to_integer(
(integer_action_to_vector(policy0[s0], 1)[0],
integer_action_to_vector(policy1[s1], 1)[0]))
# SSS
# SSS
# S<<
policy2 = {
0: ACTIONS.index(STAY),
1: ACTIONS.index(STAY),
2: ACTIONS.index(STAY),
3: ACTIONS.index(STAY),
4: ACTIONS.index(STAY),
5: ACTIONS.index(LEFT),
6: ACTIONS.index(STAY),
7: ACTIONS.index(STAY),
8: ACTIONS.index(LEFT),
}
joint_policy = CrossedPolicy(env, [
DictPolicy(env01, 1.0, policy01),
DictPolicy(get_local_view(env, [2]), 1.0, policy2)
], [[0, 1], [2]])
aux_local_env = get_local_view(env, [0])
# Assert a conflict is found for agents 1 and 2
self.assertEqual(
couple_detect_conflict(env, joint_policy, 2, 1),
((2, aux_local_env.locations_to_state(
((2, 2), )), aux_local_env.locations_to_state(((2, 1), ))),
(1, aux_local_env.locations_to_state(
((2, 0), )), aux_local_env.locations_to_state(((2, 1), )))))
# Assert no conflict is found for agents 0 and 1
self.assertIsNone(couple_detect_conflict(env, joint_policy, 0, 1))
# Assert no conflict is found for agents 0 and 2
self.assertIsNone(couple_detect_conflict(env, joint_policy, 0, 2))
def test_couple_detect_conflict_3_agents(self):
"""This test may sometime be used to test detecting a conflict for only a couple of agents.
The test will make sure that agent 0 got no conflicts with 1 and 2 while agents 1 and 2 do get a conflict.
"""
grid = MapfGrid(['...', '...', '...'])
agents_starts = ((0, 0), (2, 0), (2, 2))
agents_goals = ((0, 2), (2, 2), (2, 0))
env = MapfEnv(grid, 3, agents_starts, agents_goals, 0, 0, -1, 1, -0.01)
# >>S
# SSS
# SSS
policy0 = {
0: ACTIONS.index(RIGHT),
1: ACTIONS.index(STAY),
2: ACTIONS.index(STAY),
3: ACTIONS.index(RIGHT),
4: ACTIONS.index(STAY),
5: ACTIONS.index(STAY),
6: ACTIONS.index(STAY),
7: ACTIONS.index(STAY),
8: ACTIONS.index(STAY),
}
# SSS
# SSS
# >>S
policy1 = {
0: ACTIONS.index(STAY),
1: ACTIONS.index(STAY),
2: ACTIONS.index(RIGHT),
3: ACTIONS.index(STAY),
4: ACTIONS.index(STAY),
5: ACTIONS.index(RIGHT),
6: ACTIONS.index(STAY),
7: ACTIONS.index(STAY),
8: ACTIONS.index(STAY),
}
# SSS
# SSS
# S<<
policy2 = {
0: ACTIONS.index(STAY),
1: ACTIONS.index(STAY),
2: ACTIONS.index(STAY),
3: ACTIONS.index(STAY),
4: ACTIONS.index(STAY),
5: ACTIONS.index(LEFT),
6: ACTIONS.index(STAY),
7: ACTIONS.index(STAY),
8: ACTIONS.index(LEFT),
}
joint_policy = CrossedPolicy(env, [
DictPolicy(get_local_view(env, [0]), 1.0, policy0),
DictPolicy(get_local_view(env, [1]), 1.0, policy1),
DictPolicy(get_local_view(env, [2]), 1.0, policy2)
], [[0], [1], [2]])
aux_local_env = get_local_view(env, [0])
# Assert a conflict is found for agents 1 and 2
self.assertEqual(
couple_detect_conflict(env, joint_policy, 1, 2),
((1, aux_local_env.locations_to_state(
((2, 0), )), aux_local_env.locations_to_state(((2, 1), ))),
(2, aux_local_env.locations_to_state(
((2, 2), )), aux_local_env.locations_to_state(((2, 1), )))))
# Assert no conflict is found for agents 0 and 1
self.assertIsNone(couple_detect_conflict(env, joint_policy, 0, 1))
# Assert no conflict is found for agents 0 and 2
self.assertIsNone(couple_detect_conflict(env, joint_policy, 0, 2))