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Commit d3479be5 authored by Egli Adrian (IT-SCI-API-PFI)'s avatar Egli Adrian (IT-SCI-API-PFI)
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reinforcement_learning/multi_policy.py
+ 11
− 12
View file @ d3479be5
...@@ -4,7 +4,6 @@ from flatland.envs.rail_env import RailEnvActions ...@@ -4,7 +4,6 @@ from flatland.envs.rail_env import RailEnvActions
from reinforcement_learning.policy import Policy from reinforcement_learning.policy import Policy
from reinforcement_learning.ppo.ppo_agent import PPOAgent from reinforcement_learning.ppo.ppo_agent import PPOAgent
from utils.dead_lock_avoidance_agent import DeadLockAvoidanceAgent from utils.dead_lock_avoidance_agent import DeadLockAvoidanceAgent
from utils.extra import ExtraPolicy
class MultiPolicy(Policy): class MultiPolicy(Policy):
...@@ -13,20 +12,20 @@ class MultiPolicy(Policy): ...@@ -13,20 +12,20 @@ class MultiPolicy(Policy):
self.action_size = action_size self.action_size = action_size
self.memory = [] self.memory = []
self.loss = 0 self.loss = 0
self.extra_policy = ExtraPolicy(state_size, action_size) self.deadlock_avoidance_policy = DeadLockAvoidanceAgent(env, action_size, False)
self.ppo_policy = PPOAgent(state_size + action_size, action_size, n_agents, env) self.ppo_policy = PPOAgent(state_size + action_size, action_size, n_agents, env)
def load(self, filename): def load(self, filename):
self.ppo_policy.load(filename) self.ppo_policy.load(filename)
self.extra_policy.load(filename) self.deadlock_avoidance_policy.load(filename)
def save(self, filename): def save(self, filename):
self.ppo_policy.save(filename) self.ppo_policy.save(filename)
self.extra_policy.save(filename) self.deadlock_avoidance_policy.save(filename)
def step(self, handle, state, action, reward, next_state, done): def step(self, handle, state, action, reward, next_state, done):
action_extra_state = self.extra_policy.act(handle, state, 0.0) action_extra_state = self.deadlock_avoidance_policy.act(handle, state, 0.0)
action_extra_next_state = self.extra_policy.act(handle, next_state, 0.0) action_extra_next_state = self.deadlock_avoidance_policy.act(handle, next_state, 0.0)
extended_state = np.copy(state) extended_state = np.copy(state)
for action_itr in np.arange(self.action_size): for action_itr in np.arange(self.action_size):
...@@ -35,11 +34,11 @@ class MultiPolicy(Policy): ...@@ -35,11 +34,11 @@ class MultiPolicy(Policy):
for action_itr in np.arange(self.action_size): for action_itr in np.arange(self.action_size):
extended_next_state = np.append(extended_next_state, [int(action_extra_next_state == action_itr)]) extended_next_state = np.append(extended_next_state, [int(action_extra_next_state == action_itr)])
self.extra_policy.step(handle, state, action, reward, next_state, done) self.deadlock_avoidance_policy.step(handle, state, action, reward, next_state, done)
self.ppo_policy.step(handle, extended_state, action, reward, extended_next_state, done) self.ppo_policy.step(handle, extended_state, action, reward, extended_next_state, done)
def act(self, handle, state, eps=0.): def act(self, handle, state, eps=0.):
action_extra_state = self.extra_policy.act(handle, state, 0.0) action_extra_state = self.deadlock_avoidance_policy.act(handle, state, 0.0)
extended_state = np.copy(state) extended_state = np.copy(state)
for action_itr in np.arange(self.action_size): for action_itr in np.arange(self.action_size):
extended_state = np.append(extended_state, [int(action_extra_state == action_itr)]) extended_state = np.append(extended_state, [int(action_extra_state == action_itr)])
...@@ -49,16 +48,16 @@ class MultiPolicy(Policy): ...@@ -49,16 +48,16 @@ class MultiPolicy(Policy):
def reset(self): def reset(self):
self.ppo_policy.reset() self.ppo_policy.reset()
self.extra_policy.reset() self.deadlock_avoidance_policy.reset()
def test(self): def test(self):
self.ppo_policy.test() self.ppo_policy.test()
self.extra_policy.test() self.deadlock_avoidance_policy.test()
def start_step(self): def start_step(self):
self.extra_policy.start_step() self.deadlock_avoidance_policy.start_step()
self.ppo_policy.start_step() self.ppo_policy.start_step()
def end_step(self): def end_step(self):
self.extra_policy.end_step() self.deadlock_avoidance_policy.end_step()
self.ppo_policy.end_step() self.ppo_policy.end_step()
utils/dead_lock_avoidance_agent.py
+ 1
− 1
View file @ d3479be5
...@@ -67,7 +67,7 @@ class DeadlockAvoidanceShortestDistanceWalker(ShortestDistanceWalker): ...@@ -67,7 +67,7 @@ class DeadlockAvoidanceShortestDistanceWalker(ShortestDistanceWalker):
class DeadLockAvoidanceAgent(Policy): class DeadLockAvoidanceAgent(Policy):
def __init__(self, env: RailEnv, action_size, show_debug_plot=False): def _init__(self, env: RailEnv, action_size, show_debug_plot=False):
self.env = env self.env = env
self.memory = None self.memory = None
self.loss = 0 self.loss = 0
......
utils/extra.py deleted 100644 → 0
+ 0
− 366
View file @ 316a40d0
import numpy as np
from flatland.core.env_observation_builder import ObservationBuilder
from flatland.core.grid.grid4_utils import get_new_position
from flatland.envs.agent_utils import RailAgentStatus
from flatland.envs.rail_env import RailEnvActions, fast_argmax, fast_count_nonzero
from reinforcement_learning.policy import Policy
from utils.dead_lock_avoidance_agent import DeadLockAvoidanceAgent, DeadlockAvoidanceShortestDistanceWalker
class ExtraPolicy(Policy):
def __init__(self, state_size, action_size):
self.state_size = state_size
self.action_size = action_size
self.memory = []
self.loss = 0
def load(self, filename):
pass
def save(self, filename):
pass
def step(self, handle, state, action, reward, next_state, done):
pass
def act(self, handle, state, eps=0.):
a = 0
b = 4
action = RailEnvActions.STOP_MOVING
if state[2] == 1 and state[10 + a] == 0:
action = RailEnvActions.MOVE_LEFT
elif state[3] == 1 and state[11 + a] == 0:
action = RailEnvActions.MOVE_FORWARD
elif state[4] == 1 and state[12 + a] == 0:
action = RailEnvActions.MOVE_RIGHT
elif state[5] == 1 and state[13 + a] == 0:
action = RailEnvActions.MOVE_FORWARD
elif state[6] == 1 and state[10 + b] == 0:
action = RailEnvActions.MOVE_LEFT
elif state[7] == 1 and state[11 + b] == 0:
action = RailEnvActions.MOVE_FORWARD
elif state[8] == 1 and state[12 + b] == 0:
action = RailEnvActions.MOVE_RIGHT
elif state[9] == 1 and state[13 + b] == 0:
action = RailEnvActions.MOVE_FORWARD
return action
def test(self):
pass
class Extra(ObservationBuilder):
def __init__(self, max_depth):
self.max_depth = max_depth
self.observation_dim = 31
def build_data(self):
self.dead_lock_avoidance_agent = None
if self.env is not None:
self.env.dev_obs_dict = {}
self.dead_lock_avoidance_agent = DeadLockAvoidanceAgent(self.env, 5, False)
self.switches = {}
self.switches_neighbours = {}
self.debug_render_list = []
self.debug_render_path_list = []
if self.env is not None:
self.find_all_cell_where_agent_can_choose()
def find_all_cell_where_agent_can_choose(self):
switches = {}
for h in range(self.env.height):
for w in range(self.env.width):
pos = (h, w)
for dir in range(4):
possible_transitions = self.env.rail.get_transitions(*pos, dir)
num_transitions = fast_count_nonzero(possible_transitions)
if num_transitions > 1:
if pos not in switches.keys():
switches.update({pos: [dir]})
else:
switches[pos].append(dir)
switches_neighbours = {}
for h in range(self.env.height):
for w in range(self.env.width):
# look one step forward
for dir in range(4):
pos = (h, w)
possible_transitions = self.env.rail.get_transitions(*pos, dir)
for d in range(4):
if possible_transitions[d] == 1:
new_cell = get_new_position(pos, d)
if new_cell in switches.keys() and pos not in switches.keys():
if pos not in switches_neighbours.keys():
switches_neighbours.update({pos: [dir]})
else:
switches_neighbours[pos].append(dir)
self.switches = switches
self.switches_neighbours = switches_neighbours
def check_agent_descision(self, position, direction):
switches = self.switches
switches_neighbours = self.switches_neighbours
agents_on_switch = False
agents_on_switch_all = False
agents_near_to_switch = False
agents_near_to_switch_all = False
if position in switches.keys():
agents_on_switch = direction in switches[position]
agents_on_switch_all = True
if position in switches_neighbours.keys():
new_cell = get_new_position(position, direction)
if new_cell in switches.keys():
if not direction in switches[new_cell]:
agents_near_to_switch = direction in switches_neighbours[position]
else:
agents_near_to_switch = direction in switches_neighbours[position]
agents_near_to_switch_all = direction in switches_neighbours[position]
return agents_on_switch, agents_near_to_switch, agents_near_to_switch_all, agents_on_switch_all
def required_agent_descision(self):
agents_can_choose = {}
agents_on_switch = {}
agents_on_switch_all = {}
agents_near_to_switch = {}
agents_near_to_switch_all = {}
for a in range(self.env.get_num_agents()):
ret_agents_on_switch, ret_agents_near_to_switch, ret_agents_near_to_switch_all, ret_agents_on_switch_all = \
self.check_agent_descision(
self.env.agents[a].position,
self.env.agents[a].direction)
agents_on_switch.update({a: ret_agents_on_switch})
agents_on_switch_all.update({a: ret_agents_on_switch_all})
ready_to_depart = self.env.agents[a].status == RailAgentStatus.READY_TO_DEPART
agents_near_to_switch.update({a: (ret_agents_near_to_switch and not ready_to_depart)})
agents_can_choose.update({a: agents_on_switch[a] or agents_near_to_switch[a]})
agents_near_to_switch_all.update({a: (ret_agents_near_to_switch_all and not ready_to_depart)})
return agents_can_choose, agents_on_switch, agents_near_to_switch, agents_near_to_switch_all, agents_on_switch_all
def debug_render(self, env_renderer):
agents_can_choose, agents_on_switch, agents_near_to_switch, agents_near_to_switch_all = \
self.required_agent_descision()
self.env.dev_obs_dict = {}
for a in range(max(3, self.env.get_num_agents())):
self.env.dev_obs_dict.update({a: []})
selected_agent = None
if agents_can_choose[0]:
if self.env.agents[0].position is not None:
self.debug_render_list.append(self.env.agents[0].position)
else:
self.debug_render_list.append(self.env.agents[0].initial_position)
if self.env.agents[0].position is not None:
self.debug_render_path_list.append(self.env.agents[0].position)
else:
self.debug_render_path_list.append(self.env.agents[0].initial_position)
env_renderer.gl.agent_colors[0] = env_renderer.gl.rgb_s2i("FF0000")
env_renderer.gl.agent_colors[1] = env_renderer.gl.rgb_s2i("666600")
env_renderer.gl.agent_colors[2] = env_renderer.gl.rgb_s2i("006666")
env_renderer.gl.agent_colors[3] = env_renderer.gl.rgb_s2i("550000")
self.env.dev_obs_dict[0] = self.debug_render_list
self.env.dev_obs_dict[1] = self.switches.keys()
self.env.dev_obs_dict[2] = self.switches_neighbours.keys()
self.env.dev_obs_dict[3] = self.debug_render_path_list
def reset(self):
self.build_data()
return
def _check_dead_lock_at_branching_position(self, handle, new_position, branch_direction):
_, full_shortest_distance_agent_map = self.dead_lock_avoidance_agent.shortest_distance_walker.getData()
opp_agents = self.dead_lock_avoidance_agent.shortest_distance_walker.opp_agent_map.get(handle, [])
same_agents = self.dead_lock_avoidance_agent.shortest_distance_walker.same_agent_map.get(handle,[])
local_walker = DeadlockAvoidanceShortestDistanceWalker(
self.env,
self.dead_lock_avoidance_agent.shortest_distance_walker.agent_positions,
self.dead_lock_avoidance_agent.shortest_distance_walker.switches)
local_walker.walk_to_target(handle, new_position, branch_direction)
shortest_distance_agent_map, _ = self.dead_lock_avoidance_agent.shortest_distance_walker.getData()
my_shortest_path_to_check = shortest_distance_agent_map[handle]
next_step_ok = self.dead_lock_avoidance_agent.check_agent_can_move(handle,
my_shortest_path_to_check,
opp_agents,
same_agents,
full_shortest_distance_agent_map)
return next_step_ok
def _explore(self, handle, new_position, new_direction, depth=0):
has_opp_agent = 0
has_same_agent = 0
has_switch = 0
visited = []
# stop exploring (max_depth reached)
if depth >= self.max_depth:
return has_opp_agent, has_same_agent, has_switch, visited
# max_explore_steps = 100
cnt = 0
while cnt < 100:
cnt += 1
visited.append(new_position)
opp_a = self.env.agent_positions[new_position]
if opp_a != -1 and opp_a != handle:
if self.env.agents[opp_a].direction != new_direction:
# opp agent found
has_opp_agent = 1
return has_opp_agent, has_same_agent, has_switch, visited
else:
has_same_agent = 1
return has_opp_agent, has_same_agent, has_switch, visited
# convert one-hot encoding to 0,1,2,3
agents_on_switch, \
agents_near_to_switch, \
agents_near_to_switch_all, \
agents_on_switch_all = \
self.check_agent_descision(new_position, new_direction)
if agents_near_to_switch:
return has_opp_agent, has_same_agent, has_switch, visited
possible_transitions = self.env.rail.get_transitions(*new_position, new_direction)
if agents_on_switch:
f = 0
for dir_loop in range(4):
if possible_transitions[dir_loop] == 1:
f += 1
hoa, hsa, hs, v = self._explore(handle,
get_new_position(new_position, dir_loop),
dir_loop,
depth + 1)
visited.append(v)
has_opp_agent += hoa
has_same_agent += hsa
has_switch += hs
f = max(f, 1.0)
return has_opp_agent / f, has_same_agent / f, has_switch / f, visited
else:
new_direction = fast_argmax(possible_transitions)
new_position = get_new_position(new_position, new_direction)
return has_opp_agent, has_same_agent, has_switch, visited
def get(self, handle):
if handle == 0:
self.dead_lock_avoidance_agent.start_step()
# all values are [0,1]
# observation[0] : 1 path towards target (direction 0) / otherwise 0 -> path is longer or there is no path
# observation[1] : 1 path towards target (direction 1) / otherwise 0 -> path is longer or there is no path
# observation[2] : 1 path towards target (direction 2) / otherwise 0 -> path is longer or there is no path
# observation[3] : 1 path towards target (direction 3) / otherwise 0 -> path is longer or there is no path
# observation[4] : int(agent.status == RailAgentStatus.READY_TO_DEPART)
# observation[5] : int(agent.status == RailAgentStatus.ACTIVE)
# observation[6] : int(agent.status == RailAgentStatus.DONE or agent.status == RailAgentStatus.DONE_REMOVED)
# observation[7] : current agent is located at a switch, where it can take a routing decision
# observation[8] : current agent is located at a cell, where it has to take a stop-or-go decision
# observation[9] : current agent is located one step before/after a switch
# observation[10] : 1 if there is a path (track/branch) otherwise 0 (direction 0)
# observation[11] : 1 if there is a path (track/branch) otherwise 0 (direction 1)
# observation[12] : 1 if there is a path (track/branch) otherwise 0 (direction 2)
# observation[13] : 1 if there is a path (track/branch) otherwise 0 (direction 3)
# observation[14] : If there is a path with step (direction 0) and there is a agent with opposite direction -> 1
# observation[15] : If there is a path with step (direction 1) and there is a agent with opposite direction -> 1
# observation[16] : If there is a path with step (direction 2) and there is a agent with opposite direction -> 1
# observation[17] : If there is a path with step (direction 3) and there is a agent with opposite direction -> 1
# observation[18] : If there is a path with step (direction 0) and there is a agent with same direction -> 1
# observation[19] : If there is a path with step (direction 1) and there is a agent with same direction -> 1
# observation[20] : If there is a path with step (direction 2) and there is a agent with same direction -> 1
# observation[21] : If there is a path with step (direction 3) and there is a agent with same direction -> 1
# observation[22] : If there is a switch on the path which agent can not use -> 1
# observation[23] : If there is a switch on the path which agent can not use -> 1
# observation[24] : If there is a switch on the path which agent can not use -> 1
# observation[25] : If there is a switch on the path which agent can not use -> 1
# observation[26] : Is there a deadlock signal on shortest path walk(s) (direction 0)-> 1
# observation[27] : Is there a deadlock signal on shortest path walk(s) (direction 1)-> 1
# observation[28] : Is there a deadlock signal on shortest path walk(s) (direction 2)-> 1
# observation[29] : Is there a deadlock signal on shortest path walk(s) (direction 3)-> 1
# observation[30] : Is there a deadlock signal on shortest path walk(s) (current position check)-> 1
observation = np.zeros(self.observation_dim)
visited = []
agent = self.env.agents[handle]
agent_done = False
if agent.status == RailAgentStatus.READY_TO_DEPART:
agent_virtual_position = agent.initial_position
observation[4] = 1
elif agent.status == RailAgentStatus.ACTIVE:
agent_virtual_position = agent.position
observation[5] = 1
else:
observation[6] = 1
agent_virtual_position = (-1, -1)
agent_done = True
if not agent_done:
visited.append(agent_virtual_position)
distance_map = self.env.distance_map.get()
current_cell_dist = distance_map[handle,
agent_virtual_position[0], agent_virtual_position[1],
agent.direction]
possible_transitions = self.env.rail.get_transitions(*agent_virtual_position, agent.direction)
orientation = agent.direction
if fast_count_nonzero(possible_transitions) == 1:
orientation = fast_argmax(possible_transitions)
for dir_loop, branch_direction in enumerate([(orientation + dir_loop) % 4 for dir_loop in range(-1, 3)]):
if possible_transitions[branch_direction]:
new_position = get_new_position(agent_virtual_position, branch_direction)
new_cell_dist = distance_map[handle,
new_position[0], new_position[1],
branch_direction]
if not (np.math.isinf(new_cell_dist) and np.math.isinf(current_cell_dist)):
observation[dir_loop] = int(new_cell_dist < current_cell_dist)
has_opp_agent, has_same_agent, has_switch, v = self._explore(handle, new_position, branch_direction)
visited.append(v)
observation[10 + dir_loop] = 1
observation[14 + dir_loop] = has_opp_agent
observation[18 + dir_loop] = has_same_agent
observation[22 + dir_loop] = has_switch
next_step_ok = self._check_dead_lock_at_branching_position(handle, new_position, branch_direction)
if next_step_ok:
observation[26 + dir_loop] = 1
agents_on_switch, \
agents_near_to_switch, \
agents_near_to_switch_all, \
agents_on_switch_all = \
self.check_agent_descision(agent_virtual_position, agent.direction)
observation[7] = int(agents_on_switch)
observation[8] = int(agents_near_to_switch)
observation[9] = int(agents_near_to_switch_all)
observation[30] = int(self.dead_lock_avoidance_agent.act(handle, None, 0) == RailEnvActions.STOP_MOVING)
self.env.dev_obs_dict.update({handle: visited})
return observation
@staticmethod
def agent_can_choose(observation):
return observation[7] == 1 or observation[8] == 1
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