def make_env(n_substeps=5, horizon=250, deterministic_mode=False):
'''
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=1,
n_substeps=n_substeps,
horizon=horizon,
floor_size=10,
grid_size=50,
deterministic_mode=deterministic_mode,
env_no=0,
action_lims=(-250.0, 250.0))
# Add Walls
#env.add_module(RandomWalls(grid_size=5, num_rooms=2, min_room_size=5, door_size=5, low_outside_walls=True, outside_wall_rgba="1 1 1 0.1"))
# Add Agents
first_agent_placement = custom_placement
agent_placement_fn = [first_agent_placement]
env.add_module(SimpleAgent(1, placement_fn=agent_placement_fn))
env.reset()
keys_self = ['agent_qpos_qvel']
keys_mask_self = [] #['mask_aa_obs']
keys_external = [] #['agent_qpos_qvel']
keys_mask_external = []
keys_copy = []
env = AddConstantObservationsWrapper(
env, new_obs={'target_pos': np.full((1, 1), 0.0)})
keys_self += ['target_pos']
env = SimpleWrapper(env)
env = SplitMultiAgentActions(env)
#env = DiscretizeActionWrapper(env, 'action_movement', nbuckets=21)
env = SplitObservations(env,
keys_self + keys_mask_self,
keys_copy=keys_copy)
env = DiscardMujocoExceptionEpisodes(env)
env = SelectKeysWrapper(env,
keys_self=keys_self,
keys_external=keys_external,
keys_mask=keys_mask_self + keys_mask_external,
flatten=False)
return env
