vel = np.zeros(nsteps)
# run the simulation
input_state = np.zeros((1,2), dtype=np.float32)
for i in xrange(nsteps):
state = simulator.get_screenshot()
input_state[0] = state
a = policy.action((input_state,None))
simulator.act(a)
r = simulator.reward()
rtot += r
xpos[i], vel[i] = state
if simulator.episode_over():
break
return rtot, xpos, vel
mdp = MountainCar()
simulator = MDPSimulator(mdp)
net = pickle.load(open("../chimp/pre_trained_nets/mountain_car.net", "rb"))
backend = ChainerBackend(settings)
backend.set_net(net)
learner = DQNLearner(settings, backend)
policy = DQNPolicy(learner)
r, xtrace, vtrace = car_sim(300, simulator, policy, verbose=True)
p.plot(xtrace); p.plot(10.0*vtrace)
p.show()
obs = np.zeros((n_samples, ) + o_dims, dtype=np.float32)
obsp = np.zeros((n_samples, ) + o_dims, dtype=np.float32)
a = np.zeros(n_samples, dtype=np.int32)
r = np.zeros(n_samples, dtype=np.float32)
term = np.zeros(n_samples, dtype=np.bool)
for i in xrange(n_samples):
obs[i] = np.random.uniform(0.0, 1.0, o_dims)
a[i] = np.random.randint(n_actions)
obsp[i] = (obs[i] + 0.25) if a[i] == 1 else (obs[i] - 0.25)
obsp[i] = np.clip(obsp[i], 0.0, 1.0)
r[i] = np.sum(obs[i])
return obs, a, r, obsp, term
net = TestNet()
custom_learner = ChainerBackend(settings)
custom_learner.set_net(net)
learner = DQNLearner(settings, custom_learner)
policy = DQNPolicy(learner)
obst, a, r, obsp, term = make_batch(10, o_dims, n_actions)
for i in xrange(10):
ohist = (obst[i], None)
a = policy.action(ohist)
print "Test: ", i, " ", obst[i], " ", a, " ", learner.forward(
(obst[i], None))
print "TRAINING"
obs = np.zeros((n_samples,)+o_dims, dtype=np.float32)
obsp = np.zeros((n_samples,)+o_dims, dtype=np.float32)
a = np.zeros(n_samples, dtype=np.int32)
r = np.zeros(n_samples, dtype=np.float32)
term = np.zeros(n_samples, dtype=np.bool)
for i in xrange(n_samples):
obs[i] = np.random.uniform(0.0, 1.0, o_dims)
a[i] = np.random.randint(n_actions)
obsp[i] = (obs[i] + 0.25) if a[i] == 1 else (obs[i] - 0.25)
obsp[i] = np.clip(obsp[i], 0.0, 1.0)
r[i] = np.sum(obs[i])
return obs, a, r, obsp, term
net = TestNet()
custom_learner = ChainerBackend(settings)
custom_learner.set_net(net)
learner = DQNLearner(settings, custom_learner)
policy = DQNPolicy(learner)
obst, a, r, obsp, term = make_batch(10, o_dims, n_actions)
for i in xrange(10):
ohist = (obst[i], None)
a = policy.action(ohist)
print "Test: ", i, " ", obst[i], " ", a, " ", learner.forward((obst[i], None))
print "TRAINING"
for i in xrange(3000):
# initialize avg_var to prevent divide by zero
self.bn1.avg_var.fill(0.1),
self.bn2.avg_var.fill(0.1),
def __call__(self, ohist, ahist):
h = F.relu(self.l1(ohist))
h = F.relu(self.l2(h))
h = self.bn1(h, test=not self.train)
h = F.relu(self.l3(h))
h = F.relu(self.l4(h))
h = self.bn2(h, test=not self.train)
output = self.lout(h)
return output
net = CarNet()
# Initialize Learner with a Chainer backend
backend = ChainerBackend(settings)
backend.set_net(net)
learner = DQNLearner(settings, backend)
# Initialize memory
memory = ReplayMemoryHDF5(settings)
# Initialize Agent Framework
agent = DQNAgent(learner, memory, simulator, settings)
# Start training
agent.train(verbose=True)
from chimp.learners.chainer_backend import ChainerBackend # Agent Framework from chimp.agents import DQNAgent # Policy class for evaluation from chimp.utils.policies import DQNPolicy # initialize our mountain car simulator simulator = MountainCar() # initialize the netowrk net = CarNet() # Initialize the learner with a Chainer backend and out net backend = ChainerBackend(settings) # initialize with the settings dictionary backend.set_net(net) # set the net for our Chainer backend learner = DQNLearner(settings, backend) # create the learner # Initialize replay memory memory = ReplayMemoryHDF5(settings) # Initialize the DQNAgent agent = DQNAgent(learner, memory, simulator, settings) # pass in all 3 and settings # Start training agent.train(verbose=True) import chainer import chainer.functions as F import chainer.links as L
