class DPendulum:
def __init__(self):
self.pendulum = Pendulum(1)
self.pendulum.DT = DT
self.pendulum.NDT = 5
@property
def nqv(self):
return [NQ, NV]
@property
def nx(self):
return NQ * NV
@property
def nu(self):
return NU
@property
def goal(self):
return x2i(c2d([0., 0.]))
def reset(self, x=None):
if x is None:
x = [np.random.randint(0, NQ), np.random.randint(0, NV)]
else:
x = i2x(x)
assert (len(x) == 2)
self.x = x
return x2i(self.x)
def step(self, iu):
self.x = self.dynamics(self.x, iu)
reward = 1 if x2i(self.x) == self.goal else 0
return x2i(self.x), reward
def render(self):
q = d2cq(self.x[0])
self.pendulum.display(np.matrix([
q,
]))
time.sleep(self.pendulum.DT)
def dynamics(self, ix, iu):
x = np.matrix(d2c(ix)).T
u = d2cu(iu)
self.xc, _ = self.pendulum.dynamics(x, u)
return c2d(x.T.tolist()[0])
class DPendulum:
def __init__(self):
self.pendulum = Pendulum(1)
self.pendulum.DT = DT
self.pendulum.NDT = 5
@property
def nqv(self): return [NQ,NV]
@property
def nx(self): return NQ*NV
@property
def nu(self): return NU
@property
def goal(self): return x2i(c2d([0.,0.]))
def reset(self,x=None):
if x is None:
x = [ np.random.randint(0,NQ), np.random.randint(0,NV) ]
else: x = i2x(x)
assert(len(x)==2)
self.x = x
return x2i(self.x)
def step(self,iu):
self.x = self.dynamics(self.x,iu)
reward = 1 if x2i(self.x)==self.goal else 0
return x2i(self.x),reward
def render(self):
q = d2cq(self.x[0])
self.pendulum.display(np.matrix([q,]))
time.sleep(self.pendulum.DT)
def dynamics(self,ix,iu):
x = np.matrix(d2c (ix)).T
u = d2cu(iu)
self.xc,_ = self.pendulum.dynamics(x,u)
return c2d(x.T.tolist()[0])
# self.x = x
# self.u = u
# self.reward = r
# self.done = d
# self.x2 = x2
ReplayItem = namedtuple('ReplayItem', 'x u reward done x2 value')
ReplayItem.__new__.__defaults__ = (None, )
replayDeque = deque()
### Data
for d in data:
T = d.cost
for x, u, t in zip(d.X, d.U, d.T):
x2 = np.asarray(env.dynamics(np.matrix(x).T, np.matrix(u).T)[0].flat)
o = env.obs(np.matrix(x).T).flat
o2 = env.obs(np.matrix(x2).T).flat
replayDeque.append(
ReplayItem(x=o,
u=u.copy(),
reward=env.DT,
done=False,
x2=o2,
value=T - t))
#if t>T*.9: break # avoid trajectory ends
#if len(replayDeque)>BATCH_SIZE: break
replayDeque[-1] = replayDeque[-1]._replace(done=True)
print 'Done loading the motion lib'
