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pursuit_evade.py
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pursuit_evade.py
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import glob
import os
from os.path import join
from subprocess import call
import matplotlib.animation as animation
import matplotlib.pyplot as plt
import numpy as np
from gym import spaces
from gym.utils import seeding
from matplotlib.patches import Rectangle
from madrl_environments import AbstractMAEnv
from six.moves import xrange
from .utils import agent_utils
from .utils.AgentLayer import AgentLayer
from .utils.Controllers import RandomPolicy
from rltools.util import EzPickle
#################################################################
# Implements an Evade Pursuit Problem in 2D
#################################################################
class PursuitEvade(AbstractMAEnv, EzPickle):
def __init__(self, map_pool, **kwargs):
EzPickle.__init__(self, map_pool, **kwargs)
"""
In evade purusit a set of pursuers must 'tag' a set of evaders
Required arguments:
- map_matrix: the map on which agents interact
Optional arguments:
- Ally layer: list of pursuers
Opponent layer: list of evaders
Ally controller: stationary policy of ally pursuers
Ally controller: stationary policy of opponent evaders
map_matrix: the map on which agents interact
catchr: reward for 'tagging' a single evader
caughtr: reward for getting 'tagged' by a pursuer
train_pursuit: flag indicating if we are simulating pursuers or evaders
initial_config: dictionary of form
initial_config['allies']: the initial ally confidguration (matrix)
initial_config['opponents']: the initial opponent confidguration (matrix)
"""
self.sample_maps = kwargs.pop('sample_maps', False)
self.map_pool = map_pool
map_matrix = map_pool[0]
self.map_matrix = map_matrix
xs, ys = self.map_matrix.shape
self.xs = xs
self.ys = ys
self._reward_mech = kwargs.pop('reward_mech', 'global')
self.n_evaders = kwargs.pop('n_evaders', 1)
self.n_pursuers = kwargs.pop('n_pursuers', 1)
self.obs_range = kwargs.pop('obs_range', 3) # can see 3 grids around them by default
#assert self.obs_range % 2 != 0, "obs_range should be odd"
self.obs_offset = int((self.obs_range - 1) / 2)
self.flatten = kwargs.pop('flatten', True)
self.pursuers = agent_utils.create_agents(self.n_pursuers, map_matrix, self.obs_range,
flatten=self.flatten)
self.evaders = agent_utils.create_agents(self.n_evaders, map_matrix, self.obs_range,
flatten=self.flatten)
self.pursuer_layer = kwargs.pop('ally_layer', AgentLayer(xs, ys, self.pursuers))
self.evader_layer = kwargs.pop('opponent_layer', AgentLayer(xs, ys, self.evaders))
self.layer_norm = kwargs.pop('layer_norm', 10)
self.n_catch = kwargs.pop('n_catch', 2)
self.random_opponents = kwargs.pop('random_opponents', False)
self.max_opponents = kwargs.pop('max_opponents', 10)
n_act_purs = self.pursuer_layer.get_nactions(0)
n_act_ev = self.evader_layer.get_nactions(0)
self.evader_controller = kwargs.pop('evader_controller', RandomPolicy(n_act_purs))
self.pursuer_controller = kwargs.pop('pursuer_controller', RandomPolicy(n_act_ev))
self.current_agent_layer = np.zeros((xs, ys), dtype=np.int32)
self.catchr = kwargs.pop('catchr', 0.01)
self.caughtr = kwargs.pop('caughtr', -0.01)
self.term_pursuit = kwargs.pop('term_pursuit', 5.0)
self.term_evade = kwargs.pop('term_evade', -5.0)
self.urgency_reward = kwargs.pop('urgency_reward', 0.0)
self.include_id = kwargs.pop('include_id', True)
self.ally_actions = np.zeros(n_act_purs, dtype=np.int32)
self.opponent_actions = np.zeros(n_act_ev, dtype=np.int32)
self.train_pursuit = kwargs.pop('train_pursuit', True)
if self.train_pursuit:
self.low = np.array([0.0 for i in range(3 * self.obs_range**2)])
self.high = np.array([1.0 for i in range(3 * self.obs_range**2)])
if self.include_id:
self.low = np.append(self.low, 0.0)
self.high = np.append(self.high, 1.0)
self.action_space = spaces.Discrete(n_act_purs)
if self.flatten:
self.observation_space = spaces.Box(self.low, self.high)
else:
self.observation_space = spaces.Box(low=-np.inf, high=np.inf,
shape=(4, self.obs_range, self.obs_range))
self.local_obs = np.zeros(
(self.n_pursuers, 4, self.obs_range, self.obs_range)) # Nagents X 3 X xsize X ysize
self.act_dims = [n_act_purs for i in range(self.n_pursuers)]
else:
self.low = np.array([0.0 for i in range(3 * self.obs_range**2)])
self.high = np.array([1.0 for i in range(3 * self.obs_range**2)])
if self.include_id:
np.append(self.low, 0.0)
np.append(self.high, 1.0)
self.action_space = spaces.Discrete(n_act_ev)
if self.flatten:
self.observation_space = spaces.Box(self.low, self.high)
else:
self.observation_space = spaces.Box(low=-np.inf, high=np.inf,
shape=(4, self.obs_range, self.obs_range))
self.local_obs = np.zeros(
(self.n_evaders, 4, self.obs_range, self.obs_range)) # Nagents X 3 X xsize X ysize
self.act_dims = [n_act_purs for i in range(self.n_evaders)]
self.pursuers_gone = np.array([False for i in range(self.n_pursuers)])
self.evaders_gone = np.array([False for i in range(self.n_evaders)])
self.initial_config = kwargs.pop('initial_config', {})
self.surround = kwargs.pop('surround', True)
self.constraint_window = kwargs.pop('constraint_window', 1.0)
self.curriculum_remove_every = kwargs.pop('curriculum_remove_every', 500)
self.curriculum_constrain_rate = kwargs.pop('curriculum_constrain_rate', 0.0)
self.curriculum_turn_off_shaping = kwargs.pop('curriculum_turn_off_shaping', np.inf)
self.surround_mask = np.array([[-1, 0], [1, 0], [0, 1], [0, -1]])
self.model_state = np.zeros((4,) + map_matrix.shape, dtype=np.float32)
#################################################################
# The functions below are the interface with MultiAgentSiulator #
#################################################################
@property
def agents(self):
return self.pursuers
@property
def reward_mech(self):
return self._reward_mech
def seed(self, seed=None):
self.np_random, seed_ = seeding.np_random(seed)
return [seed_]
def get_param_values(self):
return self.__dict__
def reset(self):
#print "Check:", self.n_evaders, self.n_pursuers, self.catchr
self.pursuers_gone.fill(False)
self.evaders_gone.fill(False)
if self.random_opponents:
if self.train_pursuit:
self.n_evaders = self.np_random.randint(1, self.max_opponents)
else:
self.n_pursuers = self.np_random.randint(1, self.max_opponents)
if self.sample_maps:
self.map_matrix = self.map_pool[np.random.randint(len(self.map_pool))]
x_window_start = np.random.uniform(0.0, 1.0 - self.constraint_window)
y_window_start = np.random.uniform(0.0, 1.0 - self.constraint_window)
xlb, xub = int(self.xs * x_window_start), int(self.xs *
(x_window_start + self.constraint_window))
ylb, yub = int(self.ys * y_window_start), int(self.ys *
(y_window_start + self.constraint_window))
constraints = [[xlb, xub], [ylb, yub]]
self.pursuers = agent_utils.create_agents(self.n_pursuers, self.map_matrix, self.obs_range,
randinit=True, constraints=constraints)
self.pursuer_layer = AgentLayer(self.xs, self.ys, self.pursuers)
self.evaders = agent_utils.create_agents(self.n_evaders, self.map_matrix, self.obs_range,
randinit=True, constraints=constraints)
self.evader_layer = AgentLayer(self.xs, self.ys, self.evaders)
self.model_state[0] = self.map_matrix
self.model_state[1] = self.pursuer_layer.get_state_matrix()
self.model_state[2] = self.evader_layer.get_state_matrix()
if self.train_pursuit:
return self.collect_obs(self.pursuer_layer, self.pursuers_gone)
else:
return self.collect_obs(self.evader_layer, self.evaders_gone)
def step(self, actions):
"""
Step the system forward. Actions is an iterable of action indecies.
"""
rewards = self.reward()
if self.train_pursuit:
agent_layer = self.pursuer_layer
opponent_layer = self.evader_layer
opponent_controller = self.evader_controller
gone_flags = self.pursuers_gone
else:
agent_layer = self.evader_layer
opponent_layer = self.pursuer_layer
opponent_controller = self.pursuer_controller
gone_flags = self.evaders_gone
# move allies
if isinstance(actions, list) or isinstance(actions, np.ndarray):
# move all agents
for i, a in enumerate(actions):
agent_layer.move_agent(i, a)
else:
# ravel it up
act_idxs = np.unravel_index(actions, self.act_dims)
for i, a in enumerate(act_idxs):
agent_layer.move_agent(i, a)
# move opponents
for i in range(opponent_layer.n_agents()):
# controller input should be an observation, but doesn't matter right now
action = opponent_controller.act(self.model_state)
opponent_layer.move_agent(i, action)
# model state always has form: map, purusers, opponents, current agent id
self.model_state[0] = self.map_matrix
self.model_state[1] = self.pursuer_layer.get_state_matrix()
self.model_state[2] = self.evader_layer.get_state_matrix()
# remove agents that are caught
ev_remove, pr_remove, pursuers_who_remove = self.remove_agents()
obslist = self.collect_obs(agent_layer, gone_flags)
# add caught rewards
rewards += self.term_pursuit * pursuers_who_remove
# urgency reward to speed up catching
rewards += self.urgency_reward
done = self.is_terminal
if self.reward_mech == 'global':
return obslist, [rewards.mean()] * self.n_pursuers, done, {'removed': ev_remove}
return obslist, rewards, done, {'removed': ev_remove}
def update_curriculum(self, itr):
self.constraint_window += self.curriculum_constrain_rate # 0 to 1 in 500 iterations
self.constraint_window = np.clip(self.constraint_window, 0.0, 1.0)
# remove agents every 10 iter?
if itr != 0 and itr % self.curriculum_remove_every == 0 and self.n_pursuers > 4:
self.n_evaders -= 1
self.n_pursuers -= 1
if itr > self.curriculum_turn_off_shaping:
self.catchr = 0.0
def render(self, plt_delay=1.0):
plt.matshow(self.model_state[0].T, cmap=plt.get_cmap('Greys'), fignum=1)
for i in range(self.pursuer_layer.n_agents()):
x, y = self.pursuer_layer.get_position(i)
plt.plot(x, y, "r*", markersize=12)
if self.train_pursuit:
ax = plt.gca()
ofst = self.obs_range / 2.0
ax.add_patch(
Rectangle((x - ofst, y - ofst), self.obs_range, self.obs_range, alpha=0.5,
facecolor="#FF9848"))
for i in range(self.evader_layer.n_agents()):
x, y = self.evader_layer.get_position(i)
plt.plot(x, y, "b*", markersize=12)
if not self.train_pursuit:
ax = plt.gca()
ofst = self.obs_range / 2.0
ax.add_patch(
Rectangle((x - ofst, y - ofst), self.obs_range, self.obs_range, alpha=0.5,
facecolor="#009ACD"))
plt.pause(plt_delay)
plt.clf()
def animate(self, act_fn, nsteps, file_name, rate=1.5, verbose=False):
"""
Save an animation to an mp4 file.
"""
plt.figure(0)
# run sim loop
o = self.reset()
file_path = "/".join(file_name.split("/")[0:-1])
temp_name = join(file_path, "temp_0.png")
# generate .pngs
self.save_image(temp_name)
removed = 0
for i in range(nsteps):
a = act_fn(o)
o, r, done, info = self.step(a)
temp_name = join(file_path, "temp_" + str(i + 1) + ".png")
self.save_image(temp_name)
removed += info['removed']
if verbose:
print(r, info)
if done:
break
if verbose:
print("Total removed:", removed)
# use ffmpeg to create .pngs to .mp4 movie
ffmpeg_cmd = "ffmpeg -framerate " + str(rate) + " -i " + join(
file_path, "temp_%d.png") + " -c:v libx264 -pix_fmt yuv420p " + file_name
call(ffmpeg_cmd.split())
# clean-up by removing .pngs
map(os.remove, glob.glob(join(file_path, "temp_*.png")))
def save_image(self, file_name):
plt.cla()
plt.matshow(self.model_state[0].T, cmap=plt.get_cmap('Greys'), fignum=0)
x, y = self.pursuer_layer.get_position(0)
plt.plot(x, y, "r*", markersize=12)
for i in range(self.pursuer_layer.n_agents()):
x, y = self.pursuer_layer.get_position(i)
plt.plot(x, y, "r*", markersize=12)
if self.train_pursuit:
ax = plt.gca()
ofst = self.obs_range / 2.0
ax.add_patch(
Rectangle((x - ofst, y - ofst), self.obs_range, self.obs_range, alpha=0.5,
facecolor="#FF9848"))
for i in range(self.evader_layer.n_agents()):
x, y = self.evader_layer.get_position(i)
plt.plot(x, y, "b*", markersize=12)
if not self.train_pursuit:
ax = plt.gca()
ofst = self.obs_range / 2.0
ax.add_patch(
Rectangle((x - ofst, y - ofst), self.obs_range, self.obs_range, alpha=0.5,
facecolor="#009ACD"))
xl, xh = -self.obs_offset - 1, self.xs + self.obs_offset + 1
yl, yh = -self.obs_offset - 1, self.ys + self.obs_offset + 1
plt.xlim([xl, xh])
plt.ylim([yl, yh])
plt.axis('off')
plt.savefig(file_name, dpi=200)
def reward(self):
"""
Computes the joint reward for pursuers
"""
# rewarded for each tagged evader
ps = self.pursuer_layer.get_state_matrix() # pursuer positions
es = self.evader_layer.get_state_matrix() # evader positions
# tag reward
#tagged = (ps > 0) * es
#rewards = [
# self.catchr *
# tagged[self.pursuer_layer.get_position(i)[0], self.pursuer_layer.get_position(i)[1]]
# for i in xrange(self.n_pursuers)
#]
# proximity reward
rewards = [
self.catchr * np.sum(es[np.clip(
self.pursuer_layer.get_position(i)[0] + self.surround_mask[:, 0], 0, self.xs - 1
), np.clip(
self.pursuer_layer.get_position(i)[1] + self.surround_mask[:, 1], 0, self.ys - 1)])
for i in range(self.n_pursuers)
]
return np.array(rewards)
@property
def is_terminal(self):
#ev = self.evader_layer.get_state_matrix() # evader positions
#if np.sum(ev) == 0.0:
if self.evader_layer.n_agents() == 0:
return True
return False
def update_ally_controller(self, controller):
self.ally_controller = controller
def update_opponent_controller(self, controller):
self.opponent_controller = controller
def __getstate__(self):
d = EzPickle.__getstate__(self)
d['constraint_window'] = self.constraint_window
d['n_evaders'] = self.n_evaders
d['n_pursuers'] = self.n_pursuers
d['catchr'] = self.catchr
return d
def __setstate__(self, d):
# curriculum update attributes here for parallel sampler
EzPickle.__setstate__(self, d)
self.constraint_window = d['constraint_window']
self.n_evaders = d['n_evaders']
self.n_pursuers = d['n_pursuers']
self.catchr = d['catchr']
#################################################################
def n_agents(self):
return self.pursuer_layer.n_agents()
def collect_obs(self, agent_layer, gone_flags):
obs = []
nage = 0
for i in range(self.n_agents()):
if gone_flags[i]:
obs.append(None)
else:
o = self.collect_obs_by_idx(agent_layer, nage)
obs.append(o)
nage += 1
return obs
def collect_obs_by_idx(self, agent_layer, agent_idx):
# returns a flattened array of all the observations
n = agent_layer.n_agents()
self.local_obs[agent_idx][0].fill(1.0 / self.layer_norm) # border walls set to -0.1?
xp, yp = agent_layer.get_position(agent_idx)
xlo, xhi, ylo, yhi, xolo, xohi, yolo, yohi = self.obs_clip(xp, yp)
self.local_obs[agent_idx, 0:3, xolo:xohi, yolo:yohi] = np.abs(
self.model_state[0:3, xlo:xhi, ylo:yhi]) / self.layer_norm
self.local_obs[agent_idx, 3, self.obs_range // 2, self.obs_range // 2] = float(
agent_idx) / self.n_agents()
if self.flatten:
o = self.local_obs[agent_idx][0:3].flatten()
if self.include_id:
o = np.append(o, float(agent_idx) / self.n_agents())
return o
# reshape output from (C, H, W) to (H, W, C)
#return self.local_obs[agent_idx]
return np.rollaxis(self.local_obs[agent_idx], 0, 3)
def obs_clip(self, x, y):
# :( this is a mess, beter way to do the slicing? (maybe np.ix_)
xld = x - self.obs_offset
xhd = x + self.obs_offset
yld = y - self.obs_offset
yhd = y + self.obs_offset
xlo, xhi, ylo, yhi = (np.clip(xld, 0, self.xs - 1), np.clip(xhd, 0, self.xs - 1),
np.clip(yld, 0, self.ys - 1), np.clip(yhd, 0, self.ys - 1))
xolo, yolo = abs(np.clip(xld, -self.obs_offset, 0)), abs(np.clip(yld, -self.obs_offset, 0))
xohi, yohi = xolo + (xhi - xlo), yolo + (yhi - ylo)
return xlo, xhi + 1, ylo, yhi + 1, xolo, xohi + 1, yolo, yohi + 1
def remove_agents(self):
"""
Remove agents that are caught. Return tuple (n_evader_removed, n_pursuer_removed, purs_sur)
purs_sur: bool array, which pursuers surrounded an evader
"""
n_pursuer_removed = 0
n_evader_removed = 0
removed_evade = []
removed_pursuit = []
ai = 0
rems = 0
xpur, ypur = np.nonzero(self.model_state[1])
purs_sur = np.zeros(self.n_pursuers, dtype=np.bool)
for i in range(self.n_evaders):
if self.evaders_gone[i]:
continue
x, y = self.evader_layer.get_position(ai)
if self.surround:
pos_that_catch = self.surround_mask + self.evader_layer.get_position(ai)
truths = np.array(
[np.equal([xi, yi], pos_that_catch).all(axis=1) for xi, yi in zip(xpur, ypur)])
if np.sum(truths.any(axis=0)) == self.need_to_surround(x, y):
removed_evade.append(ai - rems)
self.evaders_gone[i] = True
rems += 1
tt = truths.any(axis=1)
for j in range(self.n_pursuers):
xpp, ypp = self.pursuer_layer.get_position(j)
tes = np.concatenate((xpur[tt], ypur[tt])).reshape(2, len(xpur[tt]))
tem = tes.T == np.array([xpp, ypp])
if np.any(np.all(tem, axis=1)):
purs_sur[j] = True
ai += 1
else:
if self.model_state[1, x, y] >= self.n_catch:
# add prob remove?
removed_evade.append(ai - rems)
self.evaders_gone[i] = True
rems += 1
for j in range(self.n_pursuers):
xpp, ypp = self.pursuer_layer.get_position(j)
if xpp == x and ypp == y:
purs_sur[j] = True
ai += 1
ai = 0
for i in range(self.pursuer_layer.n_agents()):
if self.pursuers_gone[i]:
continue
x, y = self.pursuer_layer.get_position(i)
# can remove pursuers probabilitcally here?
for ridx in removed_evade:
self.evader_layer.remove_agent(ridx)
n_evader_removed += 1
for ridx in removed_pursuit:
self.pursuer_layer.remove_agent(ridx)
n_pursuer_removed += 1
return n_evader_removed, n_pursuer_removed, purs_sur
def need_to_surround(self, x, y):
"""
Compute the number of surrounding grid cells in x,y position that are open
(no wall or obstacle)
"""
tosur = 4
if x == 0 or x == (self.xs - 1):
tosur -= 1
if y == 0 or y == (self.ys - 1):
tosur -= 1
neighbors = self.surround_mask + np.array([x, y])
for n in neighbors:
xn, yn = n
if not 0 < xn < self.xs or not 0 < yn < self.ys:
continue
if self.model_state[0][xn, yn] == -1:
tosur -= 1
return tosur
#################################################################
################## Model Based Methods ##########################
#################################################################
def idx2state(self, idx):
# return the index of a state
# assume single evader for now
pos = np.unravel_index(idx, [self.xs, self.ys] * (self.n_evaders + self.n_pursuers), 'F')
s = np.zeros(self.model_state.shape)
return s
def n_states(self):
return (self.xs * self.ys) ** (self.n_evaders + self.n_pursuers)