multiagent/scenarios/simple.py (39 lines of code) (raw):
import numpy as np
from multiagent.core import World, Agent, Landmark
from multiagent.scenario import BaseScenario
class Scenario(BaseScenario):
def make_world(self):
world = World()
# 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
# add landmarks
world.landmarks = [Landmark() for i in range(1)]
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 reset_world(self, world):
# random properties for agents
for i, agent in enumerate(world.agents):
agent.color = np.array([0.25,0.25,0.25])
# random properties for landmarks
for i, landmark in enumerate(world.landmarks):
landmark.color = np.array([0.75,0.75,0.75])
world.landmarks[0].color = np.array([0.75,0.25,0.25])
# set random initial states
for agent in world.agents:
agent.state.p_pos = np.random.uniform(-1,+1, world.dim_p)
agent.state.p_vel = np.zeros(world.dim_p)
agent.state.c = np.zeros(world.dim_c)
for i, landmark in enumerate(world.landmarks):
landmark.state.p_pos = np.random.uniform(-1,+1, world.dim_p)
landmark.state.p_vel = np.zeros(world.dim_p)
def reward(self, agent, world):
dist2 = np.sum(np.square(agent.state.p_pos - world.landmarks[0].state.p_pos))
return -dist2
def observation(self, agent, world):
# get positions of all entities in this agent's reference frame
entity_pos = []
for entity in world.landmarks:
entity_pos.append(entity.state.p_pos - agent.state.p_pos)
return np.concatenate([agent.state.p_vel] + entity_pos)