def step(self, action_dict):
obs_dict = {}
reward_dict = {}
done_dict = {'__all__': False}
info_dict = {}
# Targets move (t -> t+1)
for n in range(self.nb_targets):
self.targets[n].update()
# Agents move (t -> t+1) and observe the targets
for ii, agent_id in enumerate(action_dict):
obs_dict[self.agents[ii].agent_id] = []
reward_dict[self.agents[ii].agent_id] = []
done_dict[self.agents[ii].agent_id] = []
action_vw = self.action_map[action_dict[agent_id]]
_ = self.agents[ii].update(
action_vw,
[t.state[:2] for t in self.targets[:self.nb_targets]])
observed = []
# Update beliefs of all targets
for jj in range(self.num_targets):
# Observe
obs = self.observation(self.targets[jj], self.agents[ii])
observed.append(obs[0])
self.belief_targets[jj].predict() # Belief state at t+1
if obs[0]: # if observed, update the target belief.
self.belief_targets[jj].update(obs[1],
self.agents[ii].state)
obstacles_pt = map_utils.get_closest_obstacle(
self.MAP, self.agents[ii].state)
if obstacles_pt is None:
obstacles_pt = (self.sensor_r, np.pi)
# Calculate beliefs on only assigned targets
for kk in range(self.nb_targets):
r_b, alpha_b = util.relative_distance_polar(
self.belief_targets[kk].state[:2],
xy_base=self.agents[ii].state[:2],
theta_base=self.agents[ii].state[-1])
r_dot_b, alpha_dot_b = util.relative_velocity_polar(
self.belief_targets[kk].state[:2],
self.belief_targets[kk].state[2:],
self.agents[ii].state[:2], self.agents[ii].state[-1],
action_vw[0], action_vw[1])
obs_dict[agent_id].append([
r_b, alpha_b, r_dot_b, alpha_dot_b,
np.log(LA.det(self.belief_targets[kk].cov)),
float(observed[kk]), obstacles_pt[0], obstacles_pt[1]
])
obs_dict[agent_id] = np.asarray(obs_dict[agent_id])
# Get all rewards after all agents and targets move (t -> t+1)
reward, done, mean_nlogdetcov = self.get_reward(
obstacles_pt, observed, self.is_training)
reward_dict['__all__'], done_dict['__all__'], info_dict[
'mean_nlogdetcov'] = reward, done, mean_nlogdetcov
return obs_dict, reward_dict, done_dict, info_dict
def step(self, action_dict):
obs_dict = {}
locations = []
full_state = {}
reward_dict = {}
done_dict = {'__all__': False}
info_dict = {}
# Targets move (t -> t+1)
for n in range(self.num_targets):
self.targets[n].update()
locations.append(self.targets[n].state[:2])
# Agents move (t -> t+1) and observe the targets
for ii, agent_id in enumerate(action_dict):
obs_dict[self.agents[ii].agent_id] = []
reward_dict[self.agents[ii].agent_id] = []
done_dict[self.agents[ii].agent_id] = []
action_vw = self.action_map[action_dict[agent_id]]
_ = self.agents[ii].update(action_vw,
[t.state[:2] for t in self.targets])
locations.append(self.agents[ii].state[:2])
observed = []
for jj in range(self.num_targets):
# Observe
obs = self.observation(self.targets[jj], self.agents[ii])
observed.append(obs[0])
self.belief_targets[jj].predict() # Belief state at t+1
if obs[0]: # if observed, update the target belief.
self.belief_targets[jj].update(obs[1],
self.agents[ii].state)
obstacles_pt = map_utils.get_closest_obstacle(
self.MAP, self.agents[ii].state)
if obstacles_pt is None:
obstacles_pt = (self.sensor_r, np.pi)
for kk in range(self.num_targets):
r_b, alpha_b = util.relative_distance_polar(
self.belief_targets[kk].state[:2],
xy_base=self.agents[ii].state[:2],
theta_base=self.agents[ii].state[-1])
r_dot_b, alpha_dot_b = util.relative_velocity_polar(
self.belief_targets[kk].state[:2],
self.belief_targets[kk].state[2:],
self.agents[ii].state[:2], self.agents[ii].state[-1],
action_vw[0], action_vw[1])
obs_dict[agent_id].extend([
r_b, alpha_b, r_dot_b, alpha_dot_b,
np.log(LA.det(self.belief_targets[kk].cov)),
float(observed[kk])
])
obs_dict[agent_id].extend([obstacles_pt[0], obstacles_pt[1]])
#Global state for each agent (ref is origin)
global_state = util.global_relative_measure(np.array(locations),
self.MAP.origin)
# Full state dict
for m, agent_id in enumerate(obs_dict):
for p, ids in enumerate(obs_dict):
if agent_id != ids:
# Relative location and recent action of all other agents
r, alpha = util.relative_distance_polar(
np.array(self.agents[p].state[:2]),
xy_base=self.agents[m].state[:2],
theta_base=self.agents[m].state[2])
obs_dict[agent_id].extend([r, alpha])
full_state[agent_id] = {
'obs': np.asarray(obs_dict[agent_id]),
'state': np.concatenate((obs_dict[agent_id], global_state))
}
# Get all rewards after all agents and targets move (t -> t+1)
reward, done, mean_nlogdetcov = self.get_reward(
obstacles_pt, observed, self.is_training)
reward_dict['__all__'], done_dict['__all__'], info_dict[
'mean_nlogdetcov'] = reward, done, mean_nlogdetcov
return full_state, reward_dict, done_dict, info_dict
def step(self, action_dict):
obs_dict = {}
reward_dict = {}
done_dict = {'__all__': False}
info_dict = {}
# Targets move (t -> t+1)
for n in range(self.nb_targets):
self.targets[n].update()
# Agents move (t -> t+1) and observe the targets
for ii, agent_id in enumerate(action_dict):
obs_dict[self.agents[ii].agent_id] = []
reward_dict[self.agents[ii].agent_id] = []
done_dict[self.agents[ii].agent_id] = []
action_vw = self.action_map[action_dict[agent_id]]
# Locations of all targets and agents in order to maintain a margin between them
margin_pos = [t.state[:2] for t in self.targets[:self.nb_targets]]
for p, ids in enumerate(action_dict):
if agent_id != ids:
margin_pos.append(np.array(self.agents[p].state[:2]))
_ = self.agents[ii].update(action_vw, margin_pos)
# _ = self.agents[ii].update(action_vw, [t.state[:2] for t in self.targets[:self.nb_targets]])
observed = []
# Update beliefs of all targets
for jj in range(self.num_targets):
# Observe
obs = self.observation(self.targets[jj], self.agents[ii])
observed.append(obs[0])
self.belief_targets[jj].predict() # Belief state at t+1
if obs[0]: # if observed, update the target belief.
self.belief_targets[jj].update(obs[1],
self.agents[ii].state)
# obstacles_pt = map_utils.get_closest_obstacle(self.MAP, self.agents[ii].state)
# if obstacles_pt is None:
obstacles_pt = (self.sensor_r, np.pi)
# Calculate beliefs on only assigned targets
for kk in range(self.nb_targets):
r_b, alpha_b = util.relative_distance_polar(
self.belief_targets[kk].state[:2],
xy_base=self.agents[ii].state[:2],
theta_base=self.agents[ii].state[-1])
r_dot_b, alpha_dot_b = util.relative_velocity_polar(
self.belief_targets[kk].state[:2],
self.belief_targets[kk].state[2:],
self.agents[ii].state[:2], self.agents[ii].state[-1],
action_vw[0], action_vw[1])
obs_dict[agent_id].append([
r_b, alpha_b, r_dot_b, alpha_dot_b,
np.log(LA.det(self.belief_targets[kk].cov)),
float(observed[kk]), obstacles_pt[0], obstacles_pt[1]
])
# Greedily assign agents to closest target in order, all targets assigned if agents > targets
mask = np.ones(self.nb_targets, bool)
if self.nb_targets > self.nb_agents:
oracle = 1
else:
oracle = 0
for agent_id in obs_dict:
obs_dict[agent_id] = np.asarray(obs_dict[agent_id])
if np.sum(mask) != np.maximum(
0, self.nb_targets - self.nb_agents + oracle):
idx = np.flatnonzero(mask)
close = idx[np.argmin(obs_dict[agent_id][:, 0][mask])]
obs_dict[agent_id] = obs_dict[agent_id][None, close]
mask[close] = False
# Get all rewards after all agents and targets move (t -> t+1)
reward, done, mean_nlogdetcov = self.get_reward(
obstacles_pt, observed, self.is_training)
reward_dict['__all__'], done_dict['__all__'], info_dict[
'mean_nlogdetcov'] = reward, done, mean_nlogdetcov
return obs_dict, reward_dict, done_dict, info_dict
def step(self, action_dict):
obs_dict = {}
reward_dict = {}
done_dict = {'__all__': False}
info_dict = {}
# Targets move (t -> t+1)
for n in range(self.nb_targets):
self.targets[n].update()
self.belief_targets[n].predict() # Belief state at t+1
# Agents move (t -> t+1) and observe the targets
for ii, agent_id in enumerate(action_dict):
obs_dict[self.agents[ii].agent_id] = []
reward_dict[self.agents[ii].agent_id] = []
done_dict[self.agents[ii].agent_id] = []
action_vw = self.action_map[action_dict[agent_id]]
# Locations of all targets and agents in order to maintain a margin between them
margin_pos = [t.state[:2] for t in self.targets[:self.nb_targets]]
for p, ids in enumerate(action_dict):
if agent_id != ids:
margin_pos.append(np.array(self.agents[p].state[:2]))
_ = self.agents[ii].update(action_vw, margin_pos)
# _ = self.agents[ii].update(action_vw, [t.state[:2] for t in self.targets[:self.nb_targets]])
observed = np.zeros(self.nb_targets, dtype=bool)
obstacles_pt = (self.sensor_r, np.pi)
# Update beliefs of all targets
for jj in range(self.nb_targets):
# Observe
obs, z_t = self.observation(self.targets[jj], self.agents[ii])
observed[jj] = obs
if obs: # if observed, update the target belief.
self.belief_targets[jj].update(z_t, self.agents[ii].state)
# obstacles_pt = map_utils.get_closest_obstacle(self.MAP, self.agents[ii].state)
# if obstacles_pt is None:
# Calculate beliefs on only assigned targets
# for kk in range(self.nb_targets):
r_b, alpha_b = util.relative_distance_polar(
self.belief_targets[jj].state[:2],
xy_base=self.agents[ii].state[:2],
theta_base=self.agents[ii].state[-1])
r_dot_b, alpha_dot_b = util.relative_velocity_polar(
self.belief_targets[jj].state[:2],
self.belief_targets[jj].state[2:],
self.agents[ii].state[:2], self.agents[ii].state[-1],
action_vw[0], action_vw[1])
obs_dict[agent_id].append([
r_b, alpha_b, r_dot_b, alpha_dot_b,
np.log(LA.det(self.belief_targets[jj].cov)),
float(obs), obstacles_pt[0], obstacles_pt[1]
])
obs_dict[agent_id] = np.asarray(obs_dict[agent_id])
# Get all rewards after all agents and targets move (t -> t+1)
reward, done, mean_nlogdetcov = self.get_reward(
obstacles_pt, observed, self.is_training)
reward_dict['__all__'], done_dict['__all__'], info_dict[
'mean_nlogdetcov'] = reward, done, mean_nlogdetcov
return obs_dict, reward_dict, done_dict, info_dict