def make_env()

in mae_envs/envs/shelter_construction.py [0:0]


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