def __init__(self, mbounds, sbounds):
self.m_channels = [
envs.Channel('m{}'.format(i), mb_i)
for i, mb_i in enumerate(mbounds)
]
self.s_channels = [
envs.Channel('s{}'.format(i), sb_i)
for i, sb_i in enumerate(sbounds)
]
def __init__(self, m_bounds, s_dim):
self.m = np.random.random((len(m_bounds), s_dim))
self.m_channels = [
envs.Channel('order{}'.format(i), mb_i)
for i, mb_i in enumerate(m_bounds)
]
self.s_channels = [
envs.Channel('feedback{}'.format(i)) for _ in range(s_dim)
]
def __init__(self, mbounds):
self.m_channels = [
envs.Channel('m{}'.format(i), mb_i)
for i, mb_i in enumerate(mbounds)
]
self.s_channels = [
envs.Channel('s0', (0., 1.)),
envs.Channel('s1', (-1., 1.)),
envs.Channel('s2', (3., 10.))
]
def __init__(self):
m_bounds = ((0.0, 1.0), (0.0, 1.0))
self.m_channels = [
envs.Channel(i, mb_i) for i, mb_i in enumerate(m_bounds)
]
self.s_channels = [envs.Channel(i) for i in enumerate((2, 3))]
self._cfg = scicfg.SciConfig()
self._cfg.m_channels = self.m_channels
self._cfg.s_channels = self.s_channels
self._cfg._freeze(True)
def __init__(self, m_bounds, s_dim):
self.m = np.random.random((s_dim, len(m_bounds)))
self.m_channels = [
envs.Channel('m_{}'.format(i), mb_i)
for i, mb_i in enumerate(m_bounds)
]
self.s_channels = [
envs.Channel('s_{}'.format(i)) for i in range(s_dim)
]
self._cfg = scicfg.SciConfig()
self._cfg.m_channels = self.m_channels
self._cfg.s_channels = self.s_channels
self._cfg._freeze(True)
def __init__(self, mbounds):
self.m_channels = [
envs.Channel('m_{}'.format(i), mb_i)
for i, mb_i in enumerate(mbounds)
]
self.s_channels = [
envs.Channel('s_0'),
envs.Channel('s_1'),
envs.Channel('s_3')
]
self._cfg = scicfg.SciConfig()
self._cfg.m_channels = self.m_channels
self._cfg.s_channels = self.s_channels
self._cfg._freeze(True)
def __init__(self):
m_bounds = ((0.0, 1.0), (0.0, 1.0))
self.m_channels = [
envs.Channel(i, mb_i) for i, mb_i in enumerate(m_bounds)
]
self.s_channels = [envs.Channel(i) for i in enumerate((2, 3))]
def __init__(self):
self.m_channels = [envs.Channel('m_0', ( 0.0, 1.0)),
envs.Channel('m_1', (-10.0, 0.0))]
self.s_channels = [envs.Channel('feedback0'),
envs.Channel('feedback1'),
envs.Channel('feedback3')]