论文标题
随机步行：学习发现并达到目标而无需监督
Walk the Random Walk: Learning to Discover and Reach Goals Without Supervision
论文作者
论文摘要
通过与环境进行互动而没有任何外部监督是一个重要的挑战，可以通过与环境进行互动来学习各种技能。特别是，获得可以达到任何给定状态的目标条件的代理在许多应用中都有用。我们提出了一种新的方法，用于训练这种目标条件的代理，而没有任何外部奖励或任何领域知识。我们使用随机步行来训练可及性网络，以预测两个状态之间的相似性。然后将此可达性网络用于构建目标记忆，其中包含过去的观察值，这些观察值多样化且平衡。最后，我们训练一个基于目标条件的政策网络，其目标是从目标记忆中取得的目标，并通过可及性网络和目标记忆进行奖励。当代理商发现并学习新目标时，所有组件在整个培训中都进行了更新。我们将方法应用于连续的控制导航和机器人操纵任务。
Learning a diverse set of skills by interacting with an environment without any external supervision is an important challenge. In particular, obtaining a goal-conditioned agent that can reach any given state is useful in many applications. We propose a novel method for training such a goal-conditioned agent without any external rewards or any domain knowledge. We use random walk to train a reachability network that predicts the similarity between two states. This reachability network is then used in building goal memory containing past observations that are diverse and well-balanced. Finally, we train a goal-conditioned policy network with goals sampled from the goal memory and reward it by the reachability network and the goal memory. All the components are kept updated throughout training as the agent discovers and learns new goals. We apply our method to a continuous control navigation and robotic manipulation tasks.
