class Simulation(object):
def __init__(self,location,shape,plot):
self.location = location
self.shape = shape
self.plot = plot
self.env = Environment(self.shape)
self.agent = Agent()
self.child = Child(self.env.state)
def run(self,duration):
start = datetime.now()
self.env.state = self.child.run()
self.agent.on()
if self.plot: save_figure(self.env.x,self.env.y,self.env.state,[self.agent.position[0]],[self.agent.position[1]])
while self.agent.active:
self.agent.precepts(self.env.state)
self.env.state = self.agent.effectors(self.env.state)
if self.plot: save_figure(self.env.x,self.env.y,self.env.state,[self.agent.position[0]],[self.agent.position[1]])
else: sleep(0.5)
if (datetime.now() - start).seconds >= duration:
self.agent.gohome()
self.agent.precepts(self.env.state)
self.env.state = self.agent.effectors(self.env.state)
if self.plot: save_figure(self.env.x,self.env.y,self.env.state,[self.agent.position[0]],[self.agent.position[1]])
else: sleep(0.5)
rwd, clnd = self.agent.reward, self.agent.cleaned
self.agent.reset()
return rwd, clnd
# Objects
agent1 = Agent()
agent2 = Agent()
# Initializations
writer = SummaryWriter(comment="-q-iteration")
iter_no = 0
# Q learning Algorithm
profits = np.zeros((ITER_BREAK + 2, NUM_EPISODES + 2))
for ep in range(NUM_EPISODES):
print(ep)
# 1: initialise Qs
env.reset()
agent1.reset()
agent2.reset()
iter_no = 0
s_next = 0
while True:
iter_no += 1
eps = 1 - np.exp(-BETA * (iter_no))
# 2: agents choose actions simultanously.
action1 = agent1.act(eps)
action2 = agent2.act(eps)
action = action1 * nA + action2
# 3: outcomes are calculated
s = s_next
s_next, reward_n, done, prob = env.step(action)
# 4: Bellman updates
agent1.value_update(s, action1, reward_n[0], s_next)
