def policy(self, inputs, states):
"""
We first compute the successor features and then use the goal vector to
compute the Q values.
"""
srs, states = self.sr(inputs, states)
goal = self.goal(inputs).unsqueeze(1).expand(-1, self.num_actions, -1)
q_value = torch.sum(torch.mul(goal, srs),
dim=-1).view(-1, self.num_actions)
return dict(action=comf.q_categorical(q_value)), states
def policy(self, inputs, states):
values, states = self.value(inputs, states)
q_value = values["q_value"]
return dict(action=comf.q_categorical(q_value)), states
