def make_env(n_substeps=5,
horizon=250,
deterministic_mode=False,
n_agents=2,
n_boxes=0,
n_ramps=0,
n_hiders=1,
n_seekers=1,
other_friction=0.01,
gravity=[0, 0, -50],
polar_obs=False,
start_seed=None,
cone_angle=3 / 8 * np.pi,
placement_fn=None):
'''
This make_env function is not used anywhere; it exists to provide a simple, bare-bones
example of how to construct a multi-agent environment using the modules framework.
'''
env = Base(n_agents=n_agents,
n_substeps=n_substeps,
horizon=horizon,
deterministic_mode=deterministic_mode,
start_seed=start_seed)
#env.add_module(RandomWalls(grid_size=30, num_rooms=4, min_room_size=6, door_size=2))
env.add_module(
Centersquare(grid_size=30, num_rooms=4, min_room_size=6, door_size=2))
if n_boxes > 0:
env.add_module(
Boxes(n_boxes=n_boxes, box_size=1.,
placement_fn=uniform_placement))
if n_ramps > 0:
env.add_module(Ramps(n_ramps=n_ramps))
#env.add_module(Agents(n_agents))
env.add_module(
Agents(n_agents,
color=[np.array((66., 235., 244., 255.)) / 255] * n_hiders +
[(1., 0., 0., 1.)] * n_seekers,
friction=other_friction,
polar_obs=polar_obs,
placement_fn=None))
env.reset()
keys_self = ['agent_qpos_qvel']
keys_mask_self = ['mask_aa_obs']
keys_external = ['agent_qpos_qvel']
keys_mask_external = []
env = SplitMultiAgentActions(env)
env = DiscretizeActionWrapper(env, 'action_movement')
env = AgentAgentObsMask2D(env, cone_angle=cone_angle)
env = SplitObservations(env, keys_self + keys_mask_self)
env = SelectKeysWrapper(env,
keys_self=keys_self,
keys_external=keys_external,
keys_mask=keys_mask_self + keys_mask_external,
flatten=False)
env = DiscardMujocoExceptionEpisodes(env)
return env
def make_env(n_substeps=5,
horizon=250,
deterministic_mode=False,
n_agents=3,
n_boxes=2,
n_ramps=1):
'''
This make_env function is not used anywhere; it exists to provide a simple, bare-bones
example of how to construct a multi-agent environment using the modules framework.
'''
env = Base(
n_agents=n_agents,
n_substeps=n_substeps,
horizon=horizon, #Create Base env
deterministic_mode=deterministic_mode)
env.add_module(
RandomWalls(grid_size=30, num_rooms=4, min_room_size=6,
door_size=2)) #Add walls
if n_boxes > 0: #Add Boxes
env.add_module(Boxes(n_boxes=n_boxes))
if n_ramps > 0: #Add Ramps
env.add_module(Ramps(n_ramps=n_ramps))
env.add_module(Agents(n_agents)) #Add agentes
env.reset()
keys_self = ['agent_qpos_qvel'] #Set keys
keys_mask_self = ['mask_aa_obs']
keys_external = ['agent_qpos_qvel']
keys_mask_external = []
env = SplitMultiAgentActions(env) #Attribute action space to each agent
env = DiscretizeActionWrapper(
env, 'action_movement'
) #Discretize Action Space into bins: 3 rows of 11 bins (-1 to 1) for each agent
# print(env.action_space.spaces['action_movement'].spaces[0])
# print(env.discrete_to_continuous_act_map)
env = AgentAgentObsMask2D(env) #Set vision cone for each agent
env = SplitObservations(
env, keys_self + keys_mask_self
) #Attribute observation space to each agent: separate dimensions for each agent
env = SelectKeysWrapper(env,
keys_self=keys_self,
keys_external=keys_external,
keys_mask=keys_mask_self + keys_mask_external,
flatten=False)
env = DiscardMujocoExceptionEpisodes(env)
print("=======================\n")
return env
def make_env(n_substeps=5,
horizon=250,
deterministic_mode=False,
n_agents=2,
n_boxes=2,
n_ramps=1):
'''
This make_env function is not used anywhere; it exists to provide a simple, bare-bones
example of how to construct a multi-agent environment using the modules framework.
'''
env = Base(n_agents=n_agents,
n_substeps=n_substeps,
horizon=horizon,
deterministic_mode=deterministic_mode)
env.add_module(
RandomWalls(grid_size=30, num_rooms=4, min_room_size=6, door_size=2))
if n_boxes > 0:
env.add_module(Boxes(n_boxes=n_boxes))
if n_ramps > 0:
env.add_module(Ramps(n_ramps=n_ramps))
env.add_module(Agents(n_agents))
env.reset()
keys_self = ['agent_qpos_qvel']
keys_mask_self = ['mask_aa_obs']
keys_external = ['agent_qpos_qvel']
keys_mask_external = []
env = SplitMultiAgentActions(env)
env = DiscretizeActionWrapper(env, 'action_movement')
env = AgentAgentObsMask2D(env)
env = SplitObservations(env, keys_self + keys_mask_self)
env = SelectKeysWrapper(env,
keys_self=keys_self,
keys_external=keys_external,
keys_mask=keys_mask_self + keys_mask_external,
flatten=False)
env = DiscardMujocoExceptionEpisodes(env)
return env
def make_env(n_substeps=15,
horizon=80,
deterministic_mode=False,
floor_size=6.0,
grid_size=30,
n_agents=1,
objective_diameter=[1, 1],
objective_placement='center',
num_rays_per_side=25,
shelter_reward_scale=1,
n_boxes=2,
n_elongated_boxes=0,
box_size=0.5,
box_only_z_rot=False,
lock_box=True,
grab_box=True,
grab_selective=False,
lock_grab_radius=0.25,
lock_type='any_lock_specific',
grab_exclusive=False,
grab_out_of_vision=False,
lock_out_of_vision=True,
box_floor_friction=0.2,
other_friction=0.01,
gravity=[0, 0, -50],
action_lims=(-0.9, 0.9),
polar_obs=True,
n_lidar_per_agent=0,
visualize_lidar=False,
compress_lidar_scale=None,
boxid_obs=True,
boxsize_obs=True,
team_size_obs=False,
additional_obs={}):
grab_radius_multiplier = lock_grab_radius / box_size
lock_radius_multiplier = lock_grab_radius / box_size
env = Base(n_agents=n_agents,
n_substeps=n_substeps,
horizon=horizon,
floor_size=floor_size,
grid_size=grid_size,
action_lims=action_lims,
deterministic_mode=deterministic_mode)
env.add_module(
WallScenarios(grid_size=grid_size,
door_size=2,
scenario='empty',
friction=other_friction))
if objective_placement == 'center':
objective_placement_fn = center_placement
elif objective_placement == 'uniform_away_from_walls':
objective_placement_fn = uniform_placement_middle(0.7)
env.add_module(
Cylinders(1,
diameter=objective_diameter,
height=box_size,
make_static=True,
placement_fn=objective_placement_fn))
env.add_module(
Agents(n_agents,
placement_fn=uniform_placement,
color=[np.array((66., 235., 244., 255.)) / 255] * n_agents,
friction=other_friction,
polar_obs=polar_obs))
if np.max(n_boxes) > 0:
env.add_module(
Boxes(n_boxes=n_boxes,
placement_fn=uniform_placement,
friction=box_floor_friction,
polar_obs=polar_obs,
n_elongated_boxes=n_elongated_boxes,
boxid_obs=boxid_obs,
boxsize_obs=boxsize_obs,
box_size=box_size,
box_only_z_rot=box_only_z_rot))
if n_lidar_per_agent > 0 and visualize_lidar:
env.add_module(
LidarSites(n_agents=n_agents, n_lidar_per_agent=n_lidar_per_agent))
env.add_module(AgentManipulation())
if box_floor_friction is not None:
env.add_module(FloorAttributes(friction=box_floor_friction))
env.add_module(WorldConstants(gravity=gravity))
env.reset()
keys_self = ['agent_qpos_qvel', 'hider', 'prep_obs']
keys_mask_self = ['mask_aa_obs']
keys_external = ['agent_qpos_qvel']
keys_copy = ['you_lock', 'team_lock', 'ramp_you_lock', 'ramp_team_lock']
keys_mask_external = []
env = AddConstantObservationsWrapper(env, new_obs=additional_obs)
keys_external += list(additional_obs)
keys_mask_external += [ob for ob in additional_obs if 'mask' in ob]
env = ShelterRewardWrapper(env,
num_rays_per_side=num_rays_per_side,
reward_scale=shelter_reward_scale)
env = SplitMultiAgentActions(env)
if team_size_obs:
keys_self += ['team_size']
env = TeamMembership(env, np.zeros((n_agents, )))
env = AgentAgentObsMask2D(env)
env = DiscretizeActionWrapper(env, 'action_movement')
if np.max(n_boxes) > 0:
env = AgentGeomObsMask2D(env,
pos_obs_key='box_pos',
mask_obs_key='mask_ab_obs',
geom_idxs_obs_key='box_geom_idxs')
keys_external += ['mask_ab_obs', 'box_obs']
keys_mask_external.append('mask_ab_obs')
if lock_box and np.max(n_boxes) > 0:
env = LockObjWrapper(
env,
body_names=[f'moveable_box{i}' for i in range(n_boxes)],
agent_idx_allowed_to_lock=np.arange(n_agents),
lock_type=lock_type,
radius_multiplier=lock_radius_multiplier,
obj_in_game_metadata_keys=["curr_n_boxes"],
agent_allowed_to_lock_keys=None
if lock_out_of_vision else ["mask_ab_obs"])
if grab_box and np.max(n_boxes) > 0:
env = GrabObjWrapper(env, [f'moveable_box{i}' for i in range(n_boxes)],
radius_multiplier=grab_radius_multiplier,
grab_exclusive=grab_exclusive,
obj_in_game_metadata_keys=['curr_n_boxes'])
if n_lidar_per_agent > 0:
env = Lidar(env,
n_lidar_per_agent=n_lidar_per_agent,
visualize_lidar=visualize_lidar,
compress_lidar_scale=compress_lidar_scale)
keys_copy += ['lidar']
keys_external += ['lidar']
env = SplitObservations(env,
keys_self + keys_mask_self,
keys_copy=keys_copy)
if n_agents == 1:
env = SpoofEntityWrapper(env, 2,
['agent_qpos_qvel', 'hider', 'prep_obs'],
['mask_aa_obs'])
env = SpoofEntityWrapper(env, n_boxes,
['box_obs', 'you_lock', 'team_lock', 'obj_lock'],
['mask_ab_obs'])
keys_mask_external += ['mask_ab_obs_spoof']
env = LockAllWrapper(env, remove_object_specific_lock=True)
if not grab_out_of_vision and grab_box:
env = MaskActionWrapper(env, 'action_pull',
['mask_ab_obs']) # Can only pull if in vision
if not grab_selective and grab_box:
env = GrabClosestWrapper(env)
env = DiscardMujocoExceptionEpisodes(env)
env = ConcatenateObsWrapper(
env, {
'agent_qpos_qvel': ['agent_qpos_qvel', 'hider', 'prep_obs'],
'box_obs': ['box_obs', 'you_lock', 'team_lock', 'obj_lock']
})
env = SelectKeysWrapper(env,
keys_self=keys_self,
keys_other=keys_external + keys_mask_self +
keys_mask_external)
return env
def make_env(
n_substeps=15,
n_agents=3,
floor_size=[1.5, 6],
action_lims=(-0.9, 0.9),
grid_size=60,
other_friction=0.01,
box_floor_friction=0.2,
gravity=[0, 0, -50],
horizon=1000,
horizon_lower=None,
horizon_upper=None,
prob_per_step_to_stop=0.001,
# Food
n_food=1,
n_food_cluster=1,
food_radius=0.4,
food_respawn_time=0,
max_food_health=5,
food_together_radius=0.4,
food_rew_type='selfish',
food_reward_scale=0.0,
# Health
max_agent_health=20,
health_per_food_bounds=[2.1, 2.7],
health_per_step=-1.0,
# Attacking
attack_range=0.7,
attack_damage=-5.0,
only_attack_in_front=True,
# Death
life_rew=1,
death_rew=-100,
steps_freeze_on_death=100,
# Random Teams
rusp_args={},
# ID
id_dim=16,
# Action Masking
mask_all_when_dead=True):
env = Base(
n_agents=n_agents,
n_substeps=n_substeps,
floor_size=floor_size,
horizon=
99999999999999, # Just a big number so actual horizon is done by RandomizedHorizonWrapper
action_lims=action_lims,
deterministic_mode=False,
grid_size=grid_size)
if box_floor_friction is not None:
env.add_module(FloorAttributes(friction=box_floor_friction))
env.add_module(WorldConstants(gravity=gravity))
env.add_module(
Agents(n_agents,
placement_fn=uniform_placement,
friction=other_friction))
# Food
env.metadata['food_together_radius'] = food_together_radius
assert n_food % n_food_cluster == 0
cluster_assignments = np.repeat(
np.arange(0, n_food, n_food // n_food_cluster),
n_food // n_food_cluster)
food_placement = [
close_to_other_object_placement("food", i, "food_together_radius")
for i in cluster_assignments
]
food_placement[::n_food //
n_food_cluster] = [uniform_placement] * n_food_cluster
env.add_module(Food(n_food, placement_fn=food_placement))
env.reset()
keys_self = [
'agent_qpos_qvel', 'agent_identity', 'agent_health', 'is_dead',
'time_to_alive', 'timestep'
]
keys_additional_self_vf = ['fraction_episode_done', 'horizon']
keys_copy = ['mask_is_dead']
keys_other_agents = [
'agent_qpos_qvel',
'agent_identity',
'agent_health',
'is_dead',
'time_to_alive',
]
keys_additional_other_agents_vf = []
keys_self_matrices = []
add_rew_share_observation_keys(
keys_self=keys_self,
keys_additional_self_vf=keys_additional_self_vf,
keys_other_agents=keys_other_agents,
keys_additional_other_agents_vf=keys_additional_other_agents_vf,
keys_self_matrices=keys_self_matrices,
**rusp_args)
keys_external = [
'other_agents', 'other_agents_vf', 'additional_self_vf_obs'
]
keys_self_masks = ['mask_aa_obs']
env = SplitMultiAgentActions(env)
env = DiscretizeActionWrapper(env, 'action_movement')
env = AgentAgentObsMask2D(env)
env = ZeroRews(env)
env = RandomizedHorizonWrapper(env,
lower_lim=horizon_lower or horizon,
upper_lim=horizon_upper or horizon,
prob_per_step_to_stop=prob_per_step_to_stop)
env = FoodHealthWrapper(
env,
respawn_time=(np.inf
if food_respawn_time is None else food_respawn_time),
eat_thresh=(np.inf if food_radius is None else food_radius),
max_food_health=max_food_health,
food_rew_type=food_rew_type,
reward_scale=food_reward_scale,
split_eat_between_agents=True)
keys_external += ['mask_af_obs', 'food_obs']
keys_copy.append('close_enough_to_food')
env = FoodIncreaseHealth(env,
health_per_food_bounds=health_per_food_bounds)
env = TimeDecreaseHealth(env, health_per_step=health_per_step)
# Attack action should go before Food Health wrapper, since it masks eat action
env = AttackAction(env,
attack_damage=attack_damage,
attack_range=attack_range,
only_attack_in_front=only_attack_in_front)
env = ActionOptionsWrapper(env, ['action_attack_agent', 'action_eat_food'],
{
'action_attack_agent': -1,
'action_eat_food': 0
})
env = ColorAgentsByOption(
env, 'action_choose_option',
['action_attack_agent', 'action_eat_food', 'do_nothing'])
keys_self.append('previous_choice')
keys_other_agents.append('previous_choice')
keys_self_matrices.append('attacked_me')
keys_self.append('n_attacked_me')
keys_other_agents += ['attacked_me', 'n_attacked_me']
env = AgentHealthWrapper(env,
max_health=max_agent_health,
death_rew=death_rew,
steps_freeze_on_death=steps_freeze_on_death,
life_rew=life_rew)
# This needs to come before options wrapper, so we can't group it above
env = AlwaysEatWrapper(env, agent_idx_allowed=np.arange(n_agents))
env = RUSPWrapper(env, **rusp_args)
env = RandomIdentityVector(env, vector_dim=id_dim)
env = SplitObservations(env,
keys_self + keys_additional_self_vf,
keys_copy=keys_copy,
keys_self_matrices=keys_self_matrices +
keys_self_masks)
env = ConcatenateObsWrapper(
env, {
'other_agents':
keys_other_agents,
'other_agents_vf':
['other_agents'] + keys_additional_other_agents_vf,
'additional_self_vf_obs':
[k + '_self' for k in keys_additional_self_vf]
})
env = DiscardMujocoExceptionEpisodes(env)
env = SelectKeysWrapper(env,
keys_self=keys_self,
keys_other=keys_external + keys_copy +
keys_self_masks)
env = OasisActionMasks(env, mask_all_when_dead=mask_all_when_dead)
return env
def make_env(n_substeps=15, horizon=80, deterministic_mode=False,
floor_size=6.0, grid_size=30,
n_agents=1,
n_rooms=4, random_room_number=True, scenario='empty', door_size=2,
n_sites=3, n_elongated_sites=0, site_placement='uniform_away_from_walls',
reward_infos=[{'type': 'construction_dense'}],
n_boxes=1, n_elongated_boxes=0,
n_min_boxes=None, box_size=0.5, box_only_z_rot=False,
lock_box=True, grab_box=True, grab_selective=False, lock_grab_radius=0.25,
lock_type='any_lock_specific', grab_exclusive=False,
grab_out_of_vision=False, lock_out_of_vision=True,
box_floor_friction=0.2, other_friction=0.01, gravity=[0, 0, -50],
action_lims=(-0.9, 0.9), polar_obs=True,
n_lidar_per_agent=0, visualize_lidar=False, compress_lidar_scale=None,
boxid_obs=True, boxsize_obs=True, team_size_obs=False, additional_obs={}):
grab_radius_multiplier = lock_grab_radius / box_size
lock_radius_multiplier = lock_grab_radius / box_size
if type(n_sites) not in [list, np.ndarray]:
n_sites = [n_sites, n_sites]
env = Base(n_agents=n_agents, n_substeps=n_substeps, horizon=horizon,
floor_size=floor_size, grid_size=grid_size,
action_lims=action_lims, deterministic_mode=deterministic_mode)
if scenario == 'randomwalls':
env.add_module(RandomWalls(grid_size=grid_size, num_rooms=n_rooms,
random_room_number=random_room_number, min_room_size=6,
door_size=door_size, gen_door_obs=False))
elif scenario == 'empty':
env.add_module(WallScenarios(grid_size=grid_size, door_size=door_size,
scenario='empty',
friction=other_friction))
env.add_module(Agents(n_agents,
placement_fn=uniform_placement,
color=[np.array((66., 235., 244., 255.)) / 255] * n_agents,
friction=other_friction,
polar_obs=polar_obs))
if np.max(n_boxes) > 0:
env.add_module(Boxes(n_boxes=n_boxes, placement_fn=uniform_placement,
friction=box_floor_friction, polar_obs=polar_obs,
n_elongated_boxes=n_elongated_boxes,
boxid_obs=boxid_obs, boxsize_obs=boxsize_obs,
box_size=box_size,
box_only_z_rot=box_only_z_rot,
mark_box_corners=True))
if n_sites[1] > 0:
if site_placement == 'center':
site_placement_fn = center_placement
elif site_placement == 'uniform':
site_placement_fn = uniform_placement
elif site_placement == 'uniform_away_from_walls':
site_placement_fn = uniform_placement_middle(0.85)
else:
raise ValueError(f'Site placement option: {site_placement} not implemented.'
' Please choose from center, uniform and uniform_away_from_walls.')
env.add_module(ConstructionSites(n_sites, placement_fn=site_placement_fn,
site_size=box_size, site_height=box_size / 2,
n_elongated_sites=n_elongated_sites))
if n_lidar_per_agent > 0 and visualize_lidar:
env.add_module(LidarSites(n_agents=n_agents, n_lidar_per_agent=n_lidar_per_agent))
if np.max(n_boxes) > 0 and grab_box:
env.add_module(AgentManipulation())
if box_floor_friction is not None:
env.add_module(FloorAttributes(friction=box_floor_friction))
env.add_module(WorldConstants(gravity=gravity))
env.reset()
keys_self = ['agent_qpos_qvel']
keys_mask_self = ['mask_aa_obs']
keys_external = ['agent_qpos_qvel', 'construction_site_obs']
keys_copy = ['you_lock', 'team_lock', 'ramp_you_lock', 'ramp_team_lock']
keys_mask_external = []
env = AddConstantObservationsWrapper(env, new_obs=additional_obs)
keys_external += list(additional_obs)
keys_mask_external += [ob for ob in additional_obs if 'mask' in ob]
env = SplitMultiAgentActions(env)
if team_size_obs:
keys_self += ['team_size']
env = TeamMembership(env, np.zeros((n_agents,)))
env = AgentAgentObsMask2D(env)
env = DiscretizeActionWrapper(env, 'action_movement')
if np.max(n_boxes) > 0:
env = AgentGeomObsMask2D(env, pos_obs_key='box_pos', mask_obs_key='mask_ab_obs',
geom_idxs_obs_key='box_geom_idxs')
keys_external += ['mask_ab_obs', 'box_obs']
keys_mask_external.append('mask_ab_obs')
if lock_box and np.max(n_boxes) > 0:
agent_allowed_to_lock_keys = None if lock_out_of_vision else ["mask_ab_obs"]
env = LockObjWrapper(env, body_names=[f'moveable_box{i}' for i in range(n_boxes)],
agent_idx_allowed_to_lock=np.arange(n_agents),
lock_type=lock_type,
radius_multiplier=lock_radius_multiplier,
obj_in_game_metadata_keys=["curr_n_boxes"],
agent_allowed_to_lock_keys=agent_allowed_to_lock_keys)
if grab_box and np.max(n_boxes) > 0:
env = GrabObjWrapper(env, [f'moveable_box{i}' for i in range(n_boxes)],
radius_multiplier=grab_radius_multiplier,
grab_exclusive=grab_exclusive,
obj_in_game_metadata_keys=['curr_n_boxes'])
if n_lidar_per_agent > 0:
env = Lidar(env, n_lidar_per_agent=n_lidar_per_agent, visualize_lidar=visualize_lidar,
compress_lidar_scale=compress_lidar_scale)
keys_copy += ['lidar']
keys_external += ['lidar']
env = ConstructionDistancesWrapper(env)
env = NumpyArrayRewardWrapper(env)
reward_wrappers = {
'construction_dense': ConstructionDenseRewardWrapper,
'construction_completed': ConstructionCompletedRewardWrapper,
}
for rew_info in reward_infos:
rew_type = rew_info['type']
del rew_info['type']
env = reward_wrappers[rew_type](env, **rew_info)
env = SplitObservations(env, keys_self + keys_mask_self, keys_copy=keys_copy)
if n_agents == 1:
env = SpoofEntityWrapper(env, 2, ['agent_qpos_qvel'], ['mask_aa_obs'])
env = SpoofEntityWrapper(env, n_boxes,
['box_obs', 'you_lock', 'team_lock', 'obj_lock'],
['mask_ab_obs'])
env = SpoofEntityWrapper(env, n_sites[1], ['construction_site_obs'], ['mask_acs_obs'])
keys_mask_external += ['mask_ab_obs_spoof', 'mask_acs_obs_spoof']
env = LockAllWrapper(env, remove_object_specific_lock=True)
if not grab_out_of_vision and grab_box:
env = MaskActionWrapper(env, 'action_pull', ['mask_ab_obs']) # Can only pull if in vision
if not grab_selective and grab_box:
env = GrabClosestWrapper(env)
env = DiscardMujocoExceptionEpisodes(env)
env = ConcatenateObsWrapper(env, {'agent_qpos_qvel': ['agent_qpos_qvel'],
'box_obs': ['box_obs', 'you_lock', 'team_lock', 'obj_lock']})
env = SelectKeysWrapper(env, keys_self=keys_self,
keys_external=keys_external,
keys_mask=keys_mask_self + keys_mask_external,
flatten=False)
return env
def make_env(n_substeps=15, horizon=80, deterministic_mode=False,
floor_size=6.0, grid_size=30, door_size=2,
n_hiders=1, n_seekers=1, max_n_agents=None,
n_boxes=1, n_ramps=0, n_elongated_boxes=0,
rand_num_elongated_boxes=False, n_min_boxes=None,
box_size=0.5, boxid_obs=False, box_only_z_rot=True,
rew_type='joint_zero_sum',
lock_box=True, grab_box=True, lock_ramp=True,
lock_type='any_lock_specific',
lock_grab_radius=0.25, lock_out_of_vision=True, grab_exclusive=False,
grab_out_of_vision=False, grab_selective=False,
box_floor_friction=0.2, other_friction=0.01, gravity=[0, 0, -50],
action_lims=(-0.9, 0.9), polar_obs=True,
scenario='quadrant', quadrant_game_hider_uniform_placement=False,
p_door_dropout=0.0,
n_rooms=4, random_room_number=True, prob_outside_walls=1.0,
n_lidar_per_agent=0, visualize_lidar=False, compress_lidar_scale=None,
hiders_together_radius=None, seekers_together_radius=None,
prep_fraction=0.4, prep_obs=False,
team_size_obs=False,
restrict_rect=None, penalize_objects_out=False,
n_food=0, food_radius=None, food_respawn_time=None, max_food_health=1,
food_together_radius=None, food_rew_type='selfish', eat_when_caught=False,
food_reward_scale=1.0, food_normal_centered=False, food_box_centered=False,
n_food_cluster=1):
grab_radius_multiplier = lock_grab_radius / box_size
lock_radius_multiplier = lock_grab_radius / box_size
env = Base(n_agents=n_hiders + n_seekers, n_substeps=n_substeps, horizon=horizon,
floor_size=floor_size, grid_size=grid_size,
action_lims=action_lims,
deterministic_mode=deterministic_mode)
if scenario == 'randomwalls':
env.add_module(RandomWalls(
grid_size=grid_size, num_rooms=n_rooms,
random_room_number=random_room_number, min_room_size=6,
door_size=door_size,
prob_outside_walls=prob_outside_walls, gen_door_obs=False))
box_placement_fn = uniform_placement
ramp_placement_fn = uniform_placement
cell_size = env.floor_size / grid_size
first_hider_placement = uniform_placement
if hiders_together_radius is not None:
htr_in_cells = np.ceil(hiders_together_radius / cell_size).astype(int)
env.metadata['hiders_together_radius'] = htr_in_cells
close_to_first_hider_placement = close_to_other_object_placement(
"agent", 0, "hiders_together_radius")
agent_placement_fn = [first_hider_placement] + \
[close_to_first_hider_placement] * (n_hiders - 1)
else:
agent_placement_fn = [first_hider_placement] * n_hiders
first_seeker_placement = uniform_placement
if seekers_together_radius is not None:
str_in_cells = np.ceil(seekers_together_radius / cell_size).astype(int)
env.metadata['seekers_together_radius'] = str_in_cells
close_to_first_seeker_placement = close_to_other_object_placement(
"agent", n_hiders, "seekers_together_radius")
agent_placement_fn += [first_seeker_placement] + \
[close_to_first_seeker_placement] * (n_seekers - 1)
else:
agent_placement_fn += [first_seeker_placement] * (n_seekers)
elif scenario == 'quadrant':
env.add_module(WallScenarios(grid_size=grid_size, door_size=door_size,
scenario=scenario, friction=other_friction,
p_door_dropout=p_door_dropout))
box_placement_fn = quadrant_placement
ramp_placement_fn = uniform_placement
hider_placement = uniform_placement if quadrant_game_hider_uniform_placement else quadrant_placement
agent_placement_fn = [hider_placement] * n_hiders + [outside_quadrant_placement] * n_seekers
else:
raise ValueError(f"Scenario {scenario} not supported.")
env.add_module(Agents(n_hiders + n_seekers,
placement_fn=agent_placement_fn,
color=[np.array((66., 235., 244., 255.)) / 255] * n_hiders + [(1., 0., 0., 1.)] * n_seekers,
friction=other_friction,
polar_obs=polar_obs))
if np.max(n_boxes) > 0:
env.add_module(Boxes(n_boxes=n_boxes, placement_fn=box_placement_fn,
friction=box_floor_friction, polar_obs=polar_obs,
n_elongated_boxes=n_elongated_boxes,
boxid_obs=boxid_obs, box_only_z_rot=box_only_z_rot))
if n_ramps > 0:
env.add_module(Ramps(n_ramps=n_ramps, placement_fn=ramp_placement_fn, friction=other_friction, polar_obs=polar_obs,
pad_ramp_size=(np.max(n_elongated_boxes) > 0)))
if n_lidar_per_agent > 0 and visualize_lidar:
env.add_module(LidarSites(n_agents=n_hiders + n_seekers, n_lidar_per_agent=n_lidar_per_agent))
if n_food > 0:
if scenario == 'quadrant':
first_food_placement = quadrant_placement
elif food_box_centered:
first_food_placement = uniform_placement_middle(0.25)
else:
first_food_placement = uniform_placement
if food_together_radius is not None:
cell_size = env.floor_size / grid_size
ftr_in_cells = np.ceil(food_together_radius / cell_size).astype(int)
env.metadata['food_together_radius'] = ftr_in_cells
assert n_food % n_food_cluster == 0
cluster_assignments = np.repeat(np.arange(0, n_food, n_food // n_food_cluster), n_food // n_food_cluster)
food_placement = [close_to_other_object_placement(
"food", i, "food_together_radius") for i in cluster_assignments]
food_placement[::n_food // n_food_cluster] = [first_food_placement] * n_food_cluster
else:
food_placement = first_food_placement
env.add_module(Food(n_food, placement_fn=food_placement))
env.add_module(AgentManipulation())
if box_floor_friction is not None:
env.add_module(FloorAttributes(friction=box_floor_friction))
env.add_module(WorldConstants(gravity=gravity))
env.reset()
keys_self = ['agent_qpos_qvel', 'hider', 'prep_obs']
keys_mask_self = ['mask_aa_obs']
keys_external = ['agent_qpos_qvel']
keys_copy = ['you_lock', 'team_lock', 'ramp_you_lock', 'ramp_team_lock']
keys_mask_external = []
env = SplitMultiAgentActions(env)
if team_size_obs:
keys_self += ['team_size']
env = TeamMembership(env, np.append(np.zeros((n_hiders,)), np.ones((n_seekers,))))
env = AgentAgentObsMask2D(env)
hider_obs = np.array([[1]] * n_hiders + [[0]] * n_seekers)
env = AddConstantObservationsWrapper(env, new_obs={'hider': hider_obs})
env = HideAndSeekRewardWrapper(env, n_hiders=n_hiders, n_seekers=n_seekers,
rew_type=rew_type)
if restrict_rect is not None:
env = RestrictAgentsRect(env, restrict_rect=restrict_rect, penalize_objects_out=penalize_objects_out)
env = PreparationPhase(env, prep_fraction=prep_fraction)
env = DiscretizeActionWrapper(env, 'action_movement')
if np.max(n_boxes) > 0:
env = AgentGeomObsMask2D(env, pos_obs_key='box_pos', mask_obs_key='mask_ab_obs',
geom_idxs_obs_key='box_geom_idxs')
keys_external += ['mask_ab_obs', 'box_obs']
keys_mask_external.append('mask_ab_obs')
if n_food:
env = AgentSiteObsMask2D(env, pos_obs_key='food_pos', mask_obs_key='mask_af_obs')
env = FoodHealthWrapper(env, respawn_time=(np.inf if food_respawn_time is None else food_respawn_time),
eat_thresh=(np.inf if food_radius is None else food_radius),
max_food_health=max_food_health, food_rew_type=food_rew_type,
reward_scale=food_reward_scale)
env = MaskActionWrapper(env, 'action_eat_food', ['mask_af_obs']) # Can only eat if in vision
if prep_obs:
env = MaskPrepPhaseAction(env, 'action_eat_food')
if not eat_when_caught:
env = MaskUnseenAction(env, 0, 'action_eat_food')
eat_agents = np.arange(n_hiders)
env = AlwaysEatWrapper(env, agent_idx_allowed=eat_agents)
keys_external += ['mask_af_obs', 'food_obs']
keys_mask_external.append('mask_af_obs')
if lock_box and np.max(n_boxes) > 0:
env = LockObjWrapper(env, body_names=[f'moveable_box{i}' for i in range(np.max(n_boxes))],
agent_idx_allowed_to_lock=np.arange(n_hiders+n_seekers),
lock_type=lock_type, radius_multiplier=lock_radius_multiplier,
obj_in_game_metadata_keys=["curr_n_boxes"],
agent_allowed_to_lock_keys=None if lock_out_of_vision else ["mask_ab_obs"])
if n_ramps > 0:
env = AgentGeomObsMask2D(env, pos_obs_key='ramp_pos', mask_obs_key='mask_ar_obs',
geom_idxs_obs_key='ramp_geom_idxs')
if lock_ramp:
env = LockObjWrapper(env, body_names=[f'ramp{i}:ramp' for i in range(n_ramps)],
agent_idx_allowed_to_lock=np.arange(n_hiders+n_seekers),
lock_type=lock_type, ac_obs_prefix='ramp_',
radius_multiplier=lock_radius_multiplier,
obj_in_game_metadata_keys=['curr_n_ramps'],
agent_allowed_to_lock_keys=None if lock_out_of_vision else ["mask_ar_obs"])
keys_external += ['ramp_obs']
keys_mask_external.append('mask_ar_obs')
if grab_box and (np.max(n_boxes) > 0 or n_ramps > 0):
env = GrabObjWrapper(env, [f'moveable_box{i}' for i in range(np.max(n_boxes))] + ([f"ramp{i}:ramp" for i in range(n_ramps)]),
radius_multiplier=grab_radius_multiplier,
grab_exclusive=grab_exclusive,
obj_in_game_metadata_keys=['curr_n_boxes', 'curr_n_ramps'])
if n_lidar_per_agent > 0:
env = Lidar(env, n_lidar_per_agent=n_lidar_per_agent, visualize_lidar=visualize_lidar,
compress_lidar_scale=compress_lidar_scale)
keys_copy += ['lidar']
keys_external += ['lidar']
if prep_obs:
env = TrackStatWrapper(env, np.max(n_boxes), n_ramps, n_food)
env = SplitObservations(env, keys_self + keys_mask_self, keys_copy=keys_copy)
env = SpoofEntityWrapper(env, np.max(n_boxes), ['box_obs', 'you_lock', 'team_lock', 'obj_lock'], ['mask_ab_obs'])
if n_food:
env = SpoofEntityWrapper(env, n_food, ['food_obs'], ['mask_af_obs'])
keys_mask_external += ['mask_ab_obs_spoof', 'mask_af_obs_spoof']
if max_n_agents is not None:
env = SpoofEntityWrapper(env, max_n_agents, ['agent_qpos_qvel', 'hider', 'prep_obs'], ['mask_aa_obs'])
env = LockAllWrapper(env, remove_object_specific_lock=True)
if not grab_out_of_vision and grab_box:
env = MaskActionWrapper(env, 'action_pull',
['mask_ab_obs'] + (['mask_ar_obs'] if n_ramps > 0 else []))
if not grab_selective and grab_box:
env = GrabClosestWrapper(env)
env = NoActionsInPrepPhase(env, np.arange(n_hiders, n_hiders + n_seekers))
env = DiscardMujocoExceptionEpisodes(env)
#env = ConcatenateObsWrapper(env, {'agent_qpos_qvel': ['agent_qpos_qvel', 'hider', 'prep_obs'],
# 'box_obs': ['box_obs', 'you_lock', 'team_lock', 'obj_lock'],
# 'ramp_obs': ['ramp_obs'] + (['ramp_you_lock', 'ramp_team_lock', 'ramp_obj_lock'] if lock_ramp else [])})
env = ConcatenateObsWrapper(env, {'agent_qpos_qvel': ['agent_qpos_qvel', 'hider', 'prep_obs'],
'box_obs': ['box_obs', 'you_lock', 'team_lock', 'obj_lock'],
'ramp_obs': ['ramp_obs'] + (['ramp_you_lock', 'ramp_team_lock', 'ramp_obj_lock'] if lock_ramp else [])})
env = SelectKeysWrapper(env, keys_self=keys_self,
keys_external=keys_external,
keys_mask=keys_mask_self + keys_mask_external,
flatten=False)
return env
def make_env(
n_substeps=15,
horizon=80,
deterministic_mode=False,
floor_size=6.0,
grid_size=30,
door_size=2,
n_agents=1,
fixed_agent_spawn=False,
lock_box=True,
grab_box=True,
grab_selective=False,
lock_type='any_lock_specific',
lock_grab_radius=0.25,
grab_exclusive=False,
grab_out_of_vision=False,
lock_out_of_vision=True,
box_floor_friction=0.2,
other_friction=0.01,
gravity=[0, 0, -50],
action_lims=(-0.9, 0.9),
polar_obs=True,
scenario='quadrant',
p_door_dropout=0.0,
n_rooms=4,
random_room_number=True,
n_lidar_per_agent=0,
visualize_lidar=False,
compress_lidar_scale=None,
n_boxes=2,
box_size=0.5,
box_only_z_rot=False,
boxid_obs=True,
boxsize_obs=True,
pad_ramp_size=True,
additional_obs={},
# lock-box task
task_type='all',
lock_reward=5.0,
unlock_penalty=7.0,
shaped_reward_scale=0.25,
return_threshold=0.1,
# ramps
n_ramps=0):
grab_radius_multiplier = lock_grab_radius / box_size
lock_radius_multiplier = lock_grab_radius / box_size
env = Base(n_agents=n_agents,
n_substeps=n_substeps,
floor_size=floor_size,
horizon=horizon,
action_lims=action_lims,
deterministic_mode=deterministic_mode,
grid_size=grid_size)
if scenario == 'randomwalls':
env.add_module(
RandomWalls(grid_size=grid_size,
num_rooms=n_rooms,
random_room_number=random_room_number,
min_room_size=6,
door_size=door_size,
gen_door_obs=False))
box_placement_fn = uniform_placement
ramp_placement_fn = uniform_placement
agent_placement_fn = uniform_placement if not fixed_agent_spawn else center_placement
elif scenario == 'quadrant':
env.add_module(
WallScenarios(grid_size=grid_size,
door_size=door_size,
scenario=scenario,
friction=other_friction,
p_door_dropout=p_door_dropout))
box_placement_fn = uniform_placement
ramp_placement_fn = uniform_placement
agent_placement_fn = quadrant_placement if not fixed_agent_spawn else center_placement
elif scenario == 'empty':
env.add_module(
WallScenarios(grid_size=grid_size, door_size=2, scenario='empty'))
box_placement_fn = uniform_placement
ramp_placement_fn = uniform_placement
agent_placement_fn = center_placement
elif 'var_tri' in scenario:
env.add_module(
WallScenarios(grid_size=grid_size,
door_size=door_size,
scenario='var_tri'))
ramp_placement_fn = [tri_placement(i % 3) for i in range(n_ramps)]
agent_placement_fn = center_placement if fixed_agent_spawn else \
(uniform_placement if 'uniform' in scenario else rotate_tri_placement)
box_placement_fn = uniform_placement if 'uniform' in scenario else rotate_tri_placement
else:
raise ValueError(f"Scenario {scenario} not supported.")
env.add_module(
Agents(n_agents,
placement_fn=agent_placement_fn,
color=[np.array((66., 235., 244., 255.)) / 255] * n_agents,
friction=other_friction,
polar_obs=polar_obs))
if np.max(n_boxes) > 0:
env.add_module(
Boxes(n_boxes=n_boxes,
placement_fn=box_placement_fn,
friction=box_floor_friction,
polar_obs=polar_obs,
n_elongated_boxes=0,
boxid_obs=boxid_obs,
box_only_z_rot=box_only_z_rot,
boxsize_obs=boxsize_obs))
if n_ramps > 0:
env.add_module(
Ramps(n_ramps=n_ramps,
placement_fn=ramp_placement_fn,
friction=other_friction,
polar_obs=polar_obs,
pad_ramp_size=pad_ramp_size))
if n_lidar_per_agent > 0 and visualize_lidar:
env.add_module(
LidarSites(n_agents=n_agents, n_lidar_per_agent=n_lidar_per_agent))
if np.max(n_boxes) > 0 and grab_box:
env.add_module(AgentManipulation())
if box_floor_friction is not None:
env.add_module(FloorAttributes(friction=box_floor_friction))
env.add_module(WorldConstants(gravity=gravity))
env.reset()
keys_self = ['agent_qpos_qvel', 'hider', 'prep_obs']
keys_mask_self = ['mask_aa_obs']
keys_external = ['agent_qpos_qvel']
keys_copy = ['you_lock', 'team_lock']
keys_mask_external = []
env = SplitMultiAgentActions(env)
env = TeamMembership(env, np.zeros((n_agents, )))
env = AgentAgentObsMask2D(env)
env = DiscretizeActionWrapper(env, 'action_movement')
env = NumpyArrayRewardWrapper(env)
if np.max(n_boxes) > 0:
env = AgentGeomObsMask2D(env,
pos_obs_key='box_pos',
mask_obs_key='mask_ab_obs',
geom_idxs_obs_key='box_geom_idxs')
keys_external += ['mask_ab_obs', 'box_obs']
keys_mask_external.append('mask_ab_obs')
if lock_box and np.max(n_boxes) > 0:
env = LockObjWrapper(
env,
body_names=[f'moveable_box{i}' for i in range(n_boxes)],
agent_idx_allowed_to_lock=np.arange(n_agents),
lock_type=lock_type,
radius_multiplier=lock_radius_multiplier,
obj_in_game_metadata_keys=["curr_n_boxes"],
agent_allowed_to_lock_keys=None
if lock_out_of_vision else ["mask_ab_obs"])
if n_ramps > 0:
env = AgentGeomObsMask2D(env,
pos_obs_key='ramp_pos',
mask_obs_key='mask_ar_obs',
geom_idxs_obs_key='ramp_geom_idxs')
env = LockObjWrapper(
env,
body_names=[f"ramp{i}:ramp" for i in range(n_ramps)],
agent_idx_allowed_to_lock=np.arange(n_agents),
lock_type=lock_type,
ac_obs_prefix='ramp_',
radius_multiplier=lock_radius_multiplier,
agent_allowed_to_lock_keys=None
if lock_out_of_vision else ["mask_ar_obs"])
keys_external += ['ramp_obs']
keys_mask_external += ['mask_ar_obs']
keys_copy += ['ramp_you_lock', 'ramp_team_lock']
if grab_box and np.max(n_boxes) > 0:
body_names = ([f'moveable_box{i}' for i in range(n_boxes)] +
[f"ramp{i}:ramp" for i in range(n_ramps)])
obj_in_game_meta_keys = ['curr_n_boxes'
] + (['curr_n_ramps'] if n_ramps > 0 else [])
env = GrabObjWrapper(env,
body_names=body_names,
radius_multiplier=grab_radius_multiplier,
grab_exclusive=grab_exclusive,
obj_in_game_metadata_keys=obj_in_game_meta_keys)
if n_lidar_per_agent > 0:
env = Lidar(env,
n_lidar_per_agent=n_lidar_per_agent,
visualize_lidar=visualize_lidar,
compress_lidar_scale=compress_lidar_scale)
keys_copy += ['lidar']
keys_external += ['lidar']
env = AddConstantObservationsWrapper(env, new_obs=additional_obs)
keys_external += list(additional_obs)
keys_mask_external += [ob for ob in additional_obs if 'mask' in ob]
#############################################
# lock Box Task Reward
###
env = LockObjectsTask(env,
n_objs=n_boxes,
task=task_type,
fixed_order=True,
obj_lock_obs_key='obj_lock',
obj_pos_obs_key='box_pos',
act_lock_key='action_glue',
agent_pos_key='agent_pos',
lock_reward=lock_reward,
unlock_penalty=unlock_penalty,
shaped_reward_scale=shaped_reward_scale,
return_threshold=return_threshold)
###
#############################################
env = SplitObservations(env,
keys_self + keys_mask_self,
keys_copy=keys_copy)
env = SpoofEntityWrapper(env, n_boxes,
['box_obs', 'you_lock', 'team_lock', 'obj_lock'],
['mask_ab_obs'])
keys_mask_external += ['mask_ab_obs_spoof']
if n_agents < 2:
env = SpoofEntityWrapper(env, 1,
['agent_qpos_qvel', 'hider', 'prep_obs'],
['mask_aa_obs'])
env = LockAllWrapper(env, remove_object_specific_lock=True)
if not grab_out_of_vision and grab_box:
# Can only pull if in vision
mask_keys = ['mask_ab_obs'] + (['mask_ar_obs'] if n_ramps > 0 else [])
env = MaskActionWrapper(env,
action_key='action_pull',
mask_keys=mask_keys)
if not grab_selective and grab_box:
env = GrabClosestWrapper(env)
env = DiscardMujocoExceptionEpisodes(env)
env = ConcatenateObsWrapper(
env, {
'agent_qpos_qvel': ['agent_qpos_qvel', 'hider', 'prep_obs'],
'box_obs': ['box_obs', 'you_lock', 'team_lock', 'obj_lock'],
'ramp_obs':
['ramp_obs', 'ramp_you_lock', 'ramp_team_lock', 'ramp_obj_lock']
})
env = SelectKeysWrapper(env,
keys_self=keys_self,
keys_external=keys_external,
keys_mask=keys_mask_self + keys_mask_external,
flatten=False)
return env