from typing import List, Optional, Any
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 fast_count_nonzero, fast_argmax, RailEnvActions
from utils.agent_action_config import get_flatland_full_action_size
from utils.agent_can_choose_helper import AgentCanChooseHelper
from utils.dead_lock_avoidance_agent import DeadLockAvoidanceAgent
from utils.deadlock_check import get_agent_positions, get_agent_targets
"""
LICENCE for the FastTreeObs Observation Builder
The observation can be used freely and reused for further submissions. Only the author needs to be referred to
/mentioned in any submissions - if the entire observation or parts, or the main idea is used.
Author: Adrian Egli (adrian.egli@gmail.com)
[Linkedin](https://www.researchgate.net/profile/Adrian_Egli2)
[Researchgate](https://www.linkedin.com/in/adrian-egli-733a9544/)
"""
class FastTreeObs(ObservationBuilder):
def __init__(self, max_depth: Any):
self.max_depth = max_depth
self.observation_dim = 35
self.agent_can_choose_helper = None
self.dead_lock_avoidance_agent = DeadLockAvoidanceAgent(None, get_flatland_full_action_size())
def debug_render(self, env_renderer):
agents_can_choose, agents_on_switch, agents_near_to_switch, agents_near_to_switch_all = \
self.agent_can_choose_helper.required_agent_decision()
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.agent_can_choose_helper.switches.keys()
self.env.dev_obs_dict[2] = self.agent_can_choose_helper.switches_neighbours.keys()
self.env.dev_obs_dict[3] = self.debug_render_path_list
def reset(self):
if self.agent_can_choose_helper is None:
self.agent_can_choose_helper = AgentCanChooseHelper()
self.agent_can_choose_helper.build_data(self.env)
self.debug_render_list = []
self.debug_render_path_list = []
def _explore(self, handle, new_position, new_direction, distance_map, depth=0):
has_opp_agent = 0
has_same_agent = 0
has_target = 0
has_opp_target = 0
visited = []
min_dist = distance_map[handle, new_position[0], new_position[1], new_direction]
# stop exploring (max_depth reached)
if depth >= self.max_depth:
return has_opp_agent, has_same_agent, has_target, has_opp_target, visited, min_dist
# max_explore_steps = 100 -> just to ensure that the exploration ends
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 -> stop exploring. This would be a strong signal.
has_opp_agent = 1
return has_opp_agent, has_same_agent, has_target, has_opp_target, visited, min_dist
else:
# same agent found
# the agent can follow the agent, because this agent is still moving ahead and there shouldn't
# be any dead-lock nor other issue -> agent is just walking -> if other agent has a deadlock
# this should be avoided by other agents -> one edge case would be when other agent has it's
# target on this branch -> thus the agents should scan further whether there will be an opposite
# agent walking on same track
has_same_agent = 1
# !NOT stop exploring!
return has_opp_agent, has_same_agent, has_target, has_opp_target, visited, min_dist
# agents_on_switch == TRUE -> Current cell is a switch where the agent can decide (branch) in exploration
# agent_near_to_switch == TRUE -> One cell before the switch, where the agent can decide
#
agents_on_switch, agents_near_to_switch, _, _ = \
self.agent_can_choose_helper.check_agent_decision(new_position, new_direction)
if agents_near_to_switch:
# The exploration was walking on a path where the agent can not decide
# Best option would be MOVE_FORWARD -> Skip exploring - just walking
return has_opp_agent, has_same_agent, has_target, has_opp_target, visited, min_dist
if self.env.agents[handle].target in self.agents_target:
has_opp_target = 1
if self.env.agents[handle].target == new_position:
has_target = 1
return has_opp_agent, has_same_agent, has_target, has_opp_target, visited, min_dist
possible_transitions = self.env.rail.get_transitions(*new_position, new_direction)
if agents_on_switch:
orientation = new_direction
possible_transitions_nonzero = fast_count_nonzero(possible_transitions)
if possible_transitions_nonzero == 1:
orientation = fast_argmax(possible_transitions)
for dir_loop, branch_direction in enumerate(
[(orientation + dir_loop) % 4 for dir_loop in range(-1, 3)]):
# branch the exploration path and aggregate the found information
# --- OPEN RESEARCH QUESTION ---> is this good or shall we use full detailed information as
# we did in the TreeObservation (FLATLAND) ?
if possible_transitions[dir_loop] == 1:
hoa, hsa, ht, hot, v, m_dist = self._explore(handle,
get_new_position(new_position, dir_loop),
dir_loop,
distance_map,
depth + 1)
visited.append(v)
has_opp_agent = max(hoa, has_opp_agent)
has_same_agent = max(hsa, has_same_agent)
has_target = max(has_target, ht)
has_opp_target = max(has_opp_target, hot)
min_dist = min(min_dist, m_dist)
return has_opp_agent, has_same_agent, has_target, has_opp_target, visited, min_dist
else:
new_direction = fast_argmax(possible_transitions)
new_position = get_new_position(new_position, new_direction)
min_dist = min(min_dist, distance_map[handle, new_position[0], new_position[1], new_direction])
return has_opp_agent, has_same_agent, has_target, has_opp_target, visited, min_dist
def get_many(self, handles: Optional[List[int]] = None):
self.dead_lock_avoidance_agent.reset(self.env)
self.dead_lock_avoidance_agent.start_step(False)
self.agent_positions = get_agent_positions(self.env)
self.agents_target = get_agent_targets(self.env)
observations = super().get_many(handles)
self.dead_lock_avoidance_agent.end_step(False)
return observations
def get(self, handle: int = 0):
# 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 = 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_target, has_opp_target, v, min_dist = self._explore(handle,
new_position,
branch_direction,
distance_map)
visited.append(v)
if not (np.math.isinf(min_dist) and np.math.isinf(current_cell_dist)):
observation[11 + dir_loop] = int(min_dist < current_cell_dist)
observation[15 + dir_loop] = has_opp_agent
observation[19 + dir_loop] = has_same_agent
observation[23 + dir_loop] = has_target
observation[27 + dir_loop] = has_opp_target
agents_on_switch, \
agents_near_to_switch, \
agents_near_to_switch_all, \
agents_on_switch_all = \
self.agent_can_choose_helper.check_agent_decision(agent_virtual_position, agent.direction)
observation[7] = int(agents_on_switch)
observation[8] = int(agents_on_switch_all)
observation[9] = int(agents_near_to_switch)
observation[10] = int(agents_near_to_switch_all)
action = self.dead_lock_avoidance_agent.act(handle, None, eps=0)
observation[30] = action == RailEnvActions.DO_NOTHING
observation[31] = action == RailEnvActions.MOVE_LEFT
observation[32] = action == RailEnvActions.MOVE_FORWARD
observation[33] = action == RailEnvActions.MOVE_RIGHT
observation[34] = action == RailEnvActions.STOP_MOVING
self.env.dev_obs_dict.update({handle: visited})
observation[np.isinf(observation)] = -1
observation[np.isnan(observation)] = -1
return observation