aa4ae658489c919dbca4141c7498b05ac0cac6d8,slm_lab/agent/__init__.py,AgentSpace,act,#AgentSpace#Any#,148

Before Change

    def act(self, state_space):
        data_names = ("action",)
        action_v, = self.aeb_space.init_data_v(data_names)
        for agent in self.agents:
            a = agent.a
            state_a = state_space.get(a=a)
            action_a = agent.space_act(state_a)
            action_v[a, 0:len(action_a)] = action_a
        action_space, = self.aeb_space.add(data_names, (action_v,))
        return action_space

    @lab_api

After Change

    def update(self, state, action, reward, next_state, done):
        """Update per timestep after env transitions, e.g. memory, algorithm, update agent params, train net"""
        self.body.update(state, action, reward, next_state, done)
        if util.in_eval_lab_modes():  // eval does not update agent for training
            return
        self.body.memory.update(state, action, reward, next_state, done)
        loss = self.algorithm.train()
        if not np.isnan(loss):  // set for log_summary()
            self.body.loss = loss
        explore_var = self.algorithm.update()
        return loss, explore_var

Instances