def __init__(self, env):
super(ActorCriticNet, self).__init__()
self.affine = nn.Linear(env.state_size, 128)
self.action_head = nn.Linear(128, env.action_size)
self.value_head = nn.Linear(128, 1)
self.distribution = distributions.ActionDistribution(env,
use_probs=True)
def __init__(self, env, hidden_size=512):
super(NatureCNN, self).__init__()
self.input_size = 4
self.features = atari.NatureFeatures(self.input_size, hidden_size)
self.critic = atari.NatureCritic(hidden_size)
self.actor = atari.NatureActor(hidden_size, env.action_size)
self.action_dist = distributions.ActionDistribution(env, use_probs=False)
def __init__(self, env):
super(ActorCriticNet, self).__init__()
self.actor = models.robotics.RoboticsActor(env.state_size,
env.action_size,
layer_sizes=[64, 64])
self.critic = models.robotics.RoboticsMLP(env.state_size, 1)
self.action_dist = dist.ActionDistribution(env,
use_probs=False,
reparam=False)
def __init__(self, env):
super(ActorCriticNet, self).__init__()
self.affine1 = nn.Linear(env.state_size['image'], 128)
self.action_head = nn.Linear(128, env.action_size)
self.value_head = nn.Linear(128, 1)
self.distribution = distributions.ActionDistribution(env)