def step()

in multiagent/environment.py [0:0]

• 18 lines of code
• 6 McCabe index (conditional complexity)

    def step(self, action_n):
        obs_n = []
        reward_n = []
        done_n = []
        info_n = {'n': []}
        self.agents = self.world.policy_agents
        # set action for each agent
        for i, agent in enumerate(self.agents):
            self._set_action(action_n[i], agent, self.action_space[i])
        # advance world state
        self.world.step()
        # record observation for each agent
        for agent in self.agents:
            obs_n.append(self._get_obs(agent))
            reward_n.append(self._get_reward(agent))
            done_n.append(self._get_done(agent))

            info_n['n'].append(self._get_info(agent))

        # all agents get total reward in cooperative case
        reward = np.sum(reward_n)
        if self.shared_reward:
            reward_n = [reward] * self.n

        return obs_n, reward_n, done_n, info_n