def make_world(self):
world = World()
# set any world properties first
world.dim_c = 2
num_agents = 3
num_landmarks = 3
num_obstacles = 2
world.collaborative = True
# add agents
world.agents = [Agent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.size = size_agents
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
landmark.size = size_landmarks
# add obstacles
world.obstacles = [Landmark() for i in range(num_obstacles)]
for i, landmark in enumerate(world.obstacles):
landmark.name = 'obstacles %d' % i
landmark.collide = True
landmark.movable = False
landmark.size = size_obstacles
landmark.boundary = False
world.landmarks += world.obstacles
# make initial conditions
self.reset_world(world)
return world
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 2
num_agents = 1
num_landmarks = 1
num_obstacles = 1
# add agents
world.agents = [Agent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.size = 0.08
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
# add obstacles
world.obstacles = [Landmark() for i in range(num_obstacles)]
for i, obstacle in enumerate(world.obstacles):
obstacle.name = 'obstacle %d' % i
obstacle.collide = True
obstacle.movable = False
obstacle.size = 0.30
obstacle.boundary = False
# make initial conditions
self.reset_world(world)
return world
def set_boundaries(self, world):
boundary_list = []
landmark_size = 1
edge = 1 + landmark_size
num_landmarks = int(edge * 2 / landmark_size)
for x_pos in [-edge, edge]:
for i in range(num_landmarks):
l = Landmark()
l.state.p_pos = np.array([x_pos, -1 + i * landmark_size])
boundary_list.append(l)
for y_pos in [-edge, edge]:
for i in range(num_landmarks):
l = Landmark()
l.state.p_pos = np.array([-1 + i * landmark_size, y_pos])
boundary_list.append(l)
for i, l in enumerate(boundary_list):
l.name = 'boundary %d' % i
l.collide = True
l.movable = False
l.boundary = True
l.color = np.array([0.75, 0.75, 0.75])
l.size = landmark_size
l.state.p_vel = np.zeros(world.dim_p)
return boundary_list
def make_world(self, use_reputation=True):
self.world = world = World()
self.use_reputation = use_reputation
world.dim_c = 2
self.food_source = food_source = Landmark()
food_source.name = 'food-source'
food_source.collide = True
food_source.movable = False
food_source.size = 0.2
food_source.boundary = False
food_source.color = np.array([.3, .9, .3])
self.nest = nest = Landmark()
nest.name = 'nest'
nest.collide = True
nest.movable = False
nest.size = 0.2
nest.boundary = False
nest.color = np.array([.9, .7, .5])
world.landmarks = [food_source, nest]
# Add agents.
world.agents = \
[Pleb(food_source, nest) for i in range(5)]
#- [Spy() for i in range(3)]
self.reset_world(world)
return world
def make_world(self, use_reputation=True):
self.world = world = World()
self.use_reputation = use_reputation
world.dim_c = 2
self.food_source = food_source = Landmark()
food_source.name = 'food-source'
food_source.collide = False
food_source.movable = False
food_source.size = .09
food_source.boundary = False
food_source.color = GREEN
self.nest = nest = Landmark()
nest.name = 'nest'
nest.collide = False
nest.movable = False
nest.size = .09
nest.boundary = False
nest.color = BROWN
world.landmarks = [food_source, nest]
# Add agents.
self.plebs = [Pleb(food_source, nest) for i in range(2)]
self.spies = [Spy() for i in range(1)]
world.agents = self.plebs + self.spies
self.reset_world(world)
return world
def make_world(self, mode=2):
"""Define two agents, two boxes, and two targets.
Note that world.goals are used only for hierarchical RL visualization only
Mode0: Pre-train agents to move left box
Mode1: Pre-train agents to move right box
Mode2: Train agents to move both left and right box
"""
world = World()
self.mode = mode
world.agents = [Agent() for i in range(2)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.size = 0.1
self.boxes = [Landmark() for _ in range(2)]
for i, box in enumerate(self.boxes):
box.name = 'box %d' % i
box.size = 0.25
box.collide = True
box.index = i
# Box modes for pre-training only
if self.mode == 0 and box.index == 1:
box.movable = False
elif self.mode == 1 and box.index == 0:
box.movable = False
else:
box.movable = True
# Different box mass (Box1 is 3x heavier than Box0)
if box.index == 0:
box.initial_mass = 2.
elif box.index == 1:
box.initial_mass = 6.
else:
raise ValueError()
world.landmarks.append(box)
self.targets = [Landmark() for _ in range(2)]
for i, target in enumerate(self.targets):
target.name = 'target %d' % i
target.collide = False
target.movable = False
target.size = 0.05
target.index = i
world.landmarks.append(target)
world.goals = [Goal() for i in range(len(world.agents))]
for i, goal in enumerate(world.goals):
goal.name = 'goal %d' % i
goal.collide = False
goal.movable = False
self.reset_world(world)
return world
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 4
#world.damping = 1
num_good_agents = 2
num_adversaries = 4
num_agents = num_adversaries + num_good_agents
num_landmarks = 1
num_food = 2
num_forests = 2
self.food_ate = 0
self.good_ate = 0
# add agents
world.agents = [Agent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.leader = True if i == 0 else False
agent.silent = True if i > 0 else False
agent.adversary = True if i < num_adversaries else False
agent.size = 0.075 if agent.adversary else 0.045
agent.accel = 3.0 if agent.adversary else 4.0
#agent.accel = 20.0 if agent.adversary else 25.0
agent.max_speed = 1.0 if agent.adversary else 1.3
agent.ate = False
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = True
landmark.movable = False
landmark.size = 0.1
landmark.boundary = False
landmark.ate = False
world.food = [Landmark() for i in range(num_food)]
for i, landmark in enumerate(world.food):
landmark.name = 'food %d' % i
landmark.collide = False
landmark.movable = False
landmark.size = 0.03
landmark.boundary = False
landmark.ate = False
world.forests = [Landmark() for i in range(num_forests)]
for i, landmark in enumerate(world.forests):
landmark.name = 'forest %d' % i
landmark.collide = False
landmark.movable = False
landmark.size = 0.15
landmark.boundary = False
landmark.ate = False
world.landmarks += world.food
world.landmarks += world.forests
#world.landmarks += self.set_boundaries(world) # world boundaries now penalized with negative reward
# make initial conditions
self.reset_world(world)
return world
def make_world(self, mode):
world = World()
self.mode = mode
# add agents
world.agents = [Agent() for i in range(2)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.size = 0.1
# add boxes
self.boxes = [Landmark() for _ in range(2)]
for i, box in enumerate(self.boxes):
box.name = 'box %d' % i
box.size = 0.25
box.collide = True
box.index = i
# Box movable for pretrain
if self.mode == 0 and box.index == 1:
box.movable = False
elif self.mode == 1 and box.index == 0:
box.movable = False
else:
box.movable = True
# Different box mass
if box.index == 0:
box.initial_mass = 2.
elif box.index == 1:
box.initial_mass = 6.
else:
raise ValueError()
world.landmarks.append(box)
# add targets
self.targets = [Landmark() for _ in range(2)]
for i, target in enumerate(self.targets):
target.name = 'target %d' % i
target.collide = False
target.movable = False
target.size = 0.05
target.index = i
world.landmarks.append(target)
# add goals (used only for vis)
world.goals = [Goal() for i in range(len(world.agents))]
for i, goal in enumerate(world.goals):
goal.name = 'goal %d' % i
goal.collide = False
goal.movable = False
# make initial conditions
self.reset_world(world)
return world
def make_world(self, **kwargs):
self.before_make_world(**kwargs)
world = World()
world.np_random = self.np_random
# cache kwargs in case needed in Env wrapper
world.config = kwargs
# set any world properties first
world.collaborative = True
# all entity positions are scaled/extended by size
world.size = kwargs.get("world_size", 1)
world.dim_c = kwargs.get("dim_c", 2)
num_agents = kwargs.get("num_agents", 3)
# num_good_agents = kwargs.get("num_good_agents", 1)
# num_adversaries = kwargs.get("num_adversaries", 1)
# num_agents = num_adversaries + num_good_agents
num_landmarks = kwargs.get("num_landmarks", 2)
num_balls = kwargs.get("num_balls", 1)
# add agents
world.agents = [SkilledAgent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = kwargs.get("agent_silence", True)
agent.size = 0.025
# agent.adversary = True if i < num_adversaries else False
# agent.type = "adversary" if agent.adversary else "agent"
# agent.size = 0.075 if agent.adversary else 0.05
# agent.accel = 3.0 if agent.adversary else 4.0
# agent.max_speed = 1.0 if agent.adversary else 1.3
self.change_entity_attribute(agent, world, **kwargs)
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = True
landmark.movable = False
# landmark.size = 0.075
self.change_entity_attribute(landmark, world, **kwargs)
# add balls
world.balls = [Landmark() for i in range(num_balls)]
for i, landmark in enumerate(world.balls):
landmark.name = 'ball %d' % i
landmark.collide = True
landmark.movable = True
landmark.size = 0.2
self.change_entity_attribute(landmark, world, **kwargs)
# make initial conditions
self.reset_world(world, **kwargs)
return world
def make_world(self, mode):
"""
- mode0: Pretrain from room2 to room1
- mode1: Pretrain from room1 to target
- mode2: Train from room2 to target
"""
world = World()
self.mode = mode
# add agents
world.agents = [Agent() for i in range(2)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.size = 0.1
agent.color = np.array([1.0, 0.0, 0.0]) if i == 0 else np.array(
[0.0, 1.0, 0.0])
# add boxes
self.box = Landmark()
self.box.name = 'box'
self.box.collide = True
self.box.movable = True
self.box.size = 0.25 # Radius
self.box.initial_mass = 3.
self.box.color = np.array([0.25, 0.25, 0.25])
world.landmarks.append(self.box)
# add targets
self.target = Landmark()
self.target.name = 'target'
self.target.collide = False
self.target.movable = False
self.target.size = 0.05
self.target.color = np.array([0.25, 0.25, 0.25])
world.landmarks.append(self.target)
# add borders
self.add_borders(world)
# add goals (used only for vis)
world.goals = [Goal() for i in range(2)]
for i, goal in enumerate(world.goals):
goal.name = 'goal %d' % i
goal.collide = False
goal.movable = False
goal.color = world.agents[i].color
# make initial conditions
self.reset_world(world)
return world
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 2
num_agents = self.num_agents
num_landmarks = self.num_agents
num_obstacles = 12
# generate one-hot encoding for unique hidden goals
self.one_hot_array = list(itertools.product([0, 1], repeat=num_landmarks))
# generate colours for goal identification
for _ in range(num_landmarks):
self.colours.append(np.random.uniform(-1, +1, 3))
# add agents
world.agents = [Agent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.size = 0.10
agent.color = self.colours[i]
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
landmark.color = self.colours[i]
landmark.id = self.one_hot_array[2**i]
self.occupied_landmarks.append(0)
# add obstacles
world.obstacles = [Landmark() for i in range(num_obstacles)]
for i, obstacle in enumerate(world.obstacles):
obstacle.name = 'obstacle %d' % i
obstacle.collide = True
obstacle.movable = False
obstacle.size = 0.40
obstacle.boundary = False
obstacle.color = np.array([0.25, 0.25, 0.25])
# self.create_wall(world, obstacle, 10, -0.2, -1, -0.2, -0.2)
# make initial conditions
for i, agent in enumerate(world.agents):
agent.state.p_pos = np.array(self.sample_position())
if i > 0:
self.check_for_spawn_clash(world.agents, agent)
agent.state.p_vel = np.zeros(world.dim_p)
agent.state.c = np.zeros(world.dim_c)
self.assign_goals(i, agent)
self.reset_world(world)
return world
def make_world(self):
world = HazardousWorld()
# set scenario-specific world parameters
world.collaborative = True
world.systemic_rewards = True
world.identical_rewards = self.identical_rewards
world.dim_c = 0 # observation-based communication
world.connection_reward = _CONNECTION_REWARD
world.termination_reward = _TERMINATION_REWARD
world.render_connections = True
# add landmarks. terminals first then hazards (if any)
# world.origin_terminal_landmark = RiskRewardLandmark( risk_fn=None, reward_fn=RadialReward(1.0, 0.0))
# world.destination_terminal_landmark = RiskRewardLandmark( risk_fn=None, reward_fn=RadialReward(1.0, 0.0))
world.origin_terminal_landmark = Landmark()
world.destination_terminal_landmark = Landmark()
world.landmarks = [
world.origin_terminal_landmark, world.destination_terminal_landmark
]
world.hazard_landmarks = []
for i in range(self.num_hazards):
lm = RiskRewardLandmark(risk_fn=RadialRisk(_HAZARD_SIZE,
self.hazard_risk),
reward_fn=RadialReward(_HAZARD_SIZE, 0.0))
lm.silent = True
lm.deaf = True
lm.ignore_connection_rendering = True
world.hazard_landmarks.append(lm)
world.landmarks.append(lm)
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark_%d' % i
landmark.collide = False
landmark.movable = False
landmark.size = _LANDMARK_SIZE
# properties for landmarks
if isinstance(landmark, RiskRewardLandmark) and landmark.is_hazard:
#TODO: make colors heatmap of risk probability over all bounds
landmark.color = np.array([
landmark.risk_fn.get_failure_probability(0, 0) + .1, 0, 0
])
else:
landmark.color = np.array([0.25, 0.25, 0.25])
# make initial conditions
self.reset_world(world)
return world
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 2
num_agents = self.num_agents
num_landmarks = 1
world.collaborative = False
# add agents
world.agents = [Agent(iden=i) for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.size = 0.05
agent.adversary = False
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
landmark.size = 0.03
# make initial conditions
self.reset_world(world)
world.dists = []
return world
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 3
num_landmarks = 3
world.collaborative = True
# add agents
world.agents = [Agent() for i in range(2)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = False
agent.size = 0.075
# speaker
world.agents[0].movable = False
# listener
world.agents[1].silent = True
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
landmark.size = 0.04
# make initial conditions
self.reset_world(world)
return world
def make_world(self):
# world = World(self.scripted_agents, self.observation)
world = World(None, self.observation)
self.np_rnd = np.random.RandomState(0)
# set any world properties first
world.dim_c = 2
num_good_agents = 3
num_adversaries = 2
world.num_adversaries = num_adversaries
num_agents = num_adversaries + num_good_agents
num_landmarks = 2
self.world_radius = 1
# add agents
world.agents = [Agent(), Agent(), Agent(), Agent(), Agent()]
# world.agents = [Agent(), Agent(), Agent(), Agent(action_callback)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.adversary = True if i < num_adversaries else False
agent.size = 0.075 if agent.adversary else 0.05
agent.accel = 3.0 if agent.adversary else 4.0
#agent.accel = 20.0 if agent.adversary else 25.0
agent.max_speed = 1.0 if agent.adversary else 1.3
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = True
landmark.movable = False
landmark.size = 0.2
landmark.boundary = False
# make initial conditions
self.reset_world(world)
return world
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 2
num_agents = 3
num_landmarks = 3
world.collaborative = True
# add agents
world.agents = [Agent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.size = 0.15
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
# add noise field
noise_field = create_noise_field(world.dim_p)
world.landmarks.append(noise_field)
# make initial conditions
self.reset_world(world)
return world
def make_world(self, n_agents=3, use_dense_rewards=False):
world = World()
world.use_dense_rewards = use_dense_rewards
# set any world properties first
num_agents = n_agents
num_landmarks = 1
# add agents
world.agents = [Agent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.clip_positions = np.array([[-world.scale, -world.scale],
[world.scale, world.scale]])
agent.is_colliding = {
other_agent.name: False
for other_agent in world.agents if agent is not other_agent
}
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
landmark.size *= 3
# make initial conditions
self.reset_world(world)
return world
def make_world(self, use_dense_rewards=True):
world = World()
world.use_dense_rewards = True # We always use_dense_rewards in this task
# set any world properties first
num_landmarks = 3
num_agents = 2
# add agents
world.agents = [Agent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.clip_positions = np.array([[-world.scale, -world.scale],
[world.scale, world.scale]])
agent.collide = False
if i == 0:
agent.role = "runner"
elif i == 1:
agent.role = "pursuer"
else:
raise ValueError(
'There should only be two agents in this scenario')
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
landmark.size *= 4
landmark.is_activated = False
# make initial conditions
self.reset_world(world)
return world
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 2
num_agents = 1
num_landmarks = 20
world.observing_range = 0.7
world.min_corridor = 0.06
world.collaborative = True
# add agents
world.agents = [Agent() for _ in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.size = 0.03
# add landmarks
world.landmarks = [Landmark() for _ in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
landmark.size = np.random.uniform(0.1, 0.2)
if i == (len(world.landmarks) - 1):
landmark.size = 0.03
# make initial conditions
self.reset_world(world)
return world
def make_world(self):
world = World(is_dynamic=False, position_scale=50000.0)
world.discrete_action_space = True
world.dt = 1.0
n_landmarks = 1
# add agents
world.agents = [Agent() for i in range(1)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = False
agent.silent = True
agent.fusioned_sa = LandMarkObservation(n_landmarks, agent, world)
agent.platform_action = HeadingAction()
agent.fire_action = FireAction(2)
agent.max_speed = 350.0 # 700.0
agent.min_speed = 0.8 * agent.max_speed
agent.accel = [1.0 * 9.81, 8.0]
agent.sensor = Sensor([2 * np.pi / 3], [100000.0], [2.5e-5])
# add landmarks
world.landmarks = [Landmark() for i in range(n_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
# landmark.rwr = Rwr(max_range=200000.0, min_range=2.5e-5)
# make initial conditions
self.reset_world(world)
return world
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 2
num_good_agents = 3
num_adversaries = 3
num_agents = num_adversaries + num_good_agents
num_landmarks = 2
# add agents
world.agents = [Agent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.adversary = True if i < num_adversaries else False
agent.size = 0.075 if agent.adversary else 0.05
agent.accel = 3.0 if agent.adversary else 4.0
#agent.accel = 20.0 if agent.adversary else 25.0
agent.max_speed = 1.0 if agent.adversary else 1.3
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = True
landmark.movable = False
landmark.size = 0.2
landmark.boundary = False
# make initial conditions
self.reset_world(world)
return world
def make_world(self, args):
world = World()
# set any world properties first
world.dim_c = 2
world.num_agents = args.num_agents
world.num_landmarks = args.num_landmarks
world.step_unknown = args.step_unknown
world.unknown_decay = args.unknown_decay
world.decay_episode = args.decay_episode
world.critic_full_obs = args.critic_full_obs
world.num_reset = 0
# add agents
world.agents = [Agent() for i in range(world.num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.size = 0.15
# add landmarks
world.landmarks = [Landmark() for i in range(world.num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
world.select_goal = np.random.randint(0, world.num_landmarks)
world.agents[0].goal = world.landmarks[world.select_goal]
# make initial conditions
self.reset_world(world)
return world
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 2
num_agents = 5
num_adversaries = 3
num_landmarks = 1
# add agents
world.agents = [Agent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
if i < num_adversaries:
agent.adversary = True
else:
agent.adversary = False
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
# make initial conditions
self.reset_world(world)
return world
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 2
world.agents_num = self.num_agents
num_landmarks = 0
# add agents
world.agents = [Agent() for i in range(self.num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
##############
agent.index = i
agent.collide = True
agent.silent = True
##########################
agent.adversary = True if i < self.num_adversaries else False
agent.size = agent_size if agent.adversary else adver_size
# agent.accel = 3.0 if agent.adversary else 4.0
agent.accel = 200.0 if agent.adversary else 250.0
agent.max_speed = agent_speed * 10 if agent.adversary else adver_speed * 10 #v = speed /10
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = True
landmark.movable = False
landmark.size = 0.2
landmark.boundary = False
# make initial conditions
self.reset_world(world)
return world
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 3
num_landmarks = 5
world.collaborative = True
# this will be the furthest any agent can move in any direction
world.size = 5
# add agents
world.agents = [Agent() for i in range(2)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = False
agent.size = 0.075 # I think this is irrelevant?
# speaker
world.agents[0].movable = False
# listener (discrete)
world.agents[1].silent = True
world.agents[1].hopper = True
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
landmark.size = 0.04 # I think this is irrelevant?
# make initial conditions
self.reset_world(world)
return world
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 2
world.num_agents = 4
world.num_goals = 4
# world.num_obstacles = 2
world.num_obstacles = 2
world.collaborative = True
# self.landmarkspeed = np.random.normal(size=2)
# add agents
world.agents = [Agent() for i in range(world.num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.size = 0.1
# add goals
world.landmarks = [
Landmark() for i in range(world.num_goals + world.num_obstacles)
]
p_vel = np.random.normal(size=2)
for i, landmark in enumerate(world.landmarks):
if i < world.num_goals:
landmark.name = 'goal %d' % i
landmark.collide = False
landmark.movable = False
landmark.state.p_vel = p_vel
else:
landmark.name = 'obstacle %d' % (i - world.num_goals)
landmark.collide = True
landmark.movable = False
self.reset_world(world)
return world
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 2
world.num_agents = num_agents = 1
world.num_goals = num_goals = 1
world.num_obstacles = num_obstacles = 1
world.collaborative = True
# add agents
world.agents = [Agent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.size = 0.1
# add landmarks
world.landmarks = [
Landmark() for i in range(num_goals + num_obstacles)
]
for i, landmark in enumerate(world.landmarks):
if i < num_goals:
landmark.name = 'goal %d' % i
landmark.collide = True
landmark.movable = False
else:
landmark.name = 'obstacle %d' % (i - num_goals)
landmark.size = 0.1
landmark.collide = True
landmark.movable = False
# make initial conditions
self.reset_world(world)
return world
def make_world(self, num_agents_inpt=2, num_adversaries_inpt=1):
world = World()
# set any world properties first
world.dim_c = 2
num_agents = num_agents_inpt
world.num_agents = num_agents_inpt
num_adversaries = num_adversaries_inpt
num_landmarks = num_agents - num_adversaries_inpt
# add agents
world.agents = [Agent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = False
agent.silent = True
agent.adversary = True if i < num_adversaries else False
agent.color = np.array([0.0, 0.0, 0.0]) if i < num_adversaries else np.array([1.0, 1.0, 0.0])
agent.size = 0.15
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
landmark.size = 0.08
# make initial conditions
self.reset_world(world)
return world
def make_world(self, mode=0):
world = World()
self.mode = mode
# set any world properties first
world.dim_c = 2
num_agents = 2
num_landmarks = 2
world.collaborative = True
assert num_agents == 2, "This env is only for two agents"
# add agents
world.agents = [Agent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = False
agent.silent = True
agent.size = 0.15
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
# make initial conditions
self.reset_world(world)
return world
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 2
num_agents = 3
num_landmarks = 0
world.collaborative = False
# add agents
world.agents = [Agent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.name = 'agent %d' % i
agent.collide = True
agent.silent = True
agent.size = 0.08
# add edges
world.edges = []
for i in range(len(world.agents)):
world.edges.append(1)
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = 'landmark %d' % i
landmark.collide = False
landmark.movable = False
# make initial conditions
self.reset_world(world)
return world
