标题： 环境无关的目标条件，自由奖励自主学习的研究 显示英文标题

标题： Environment Agnostic Goal-Conditioning, A Study of Reward-Free Autonomous Learning

摘要： 在本文中，我们研究如何将常规强化学习环境转化为目标条件环境，使智能体能够自主且无需奖励地解决任务。 我们表明，智能体可以在与外部引导的强化学习相当的训练时间内，以环境无关的方式选择自己的目标来学习解决问题。 我们的方法独立于底层的离策略学习算法。 由于我们的方法是环境无关的，智能体不会对任何目标给予更高的价值，这导致单个目标的性能不稳定。 然而，在我们的实验中，我们展示了平均目标成功率达到并稳定在较高水平。 使用此方法训练的智能体可以被指示去寻找环境中任何观察到的内容，从而在特定用例之前实现智能体的通用训练。 显示英文摘要

摘要： In this paper we study how transforming regular reinforcement learning environments into goal-conditioned environments can let agents learn to solve tasks autonomously and reward-free. We show that an agent can learn to solve tasks by selecting its own goals in an environment-agnostic way, at training times comparable to externally guided reinforcement learning. Our method is independent of the underlying off-policy learning algorithm. Since our method is environment-agnostic, the agent does not value any goals higher than others, leading to instability in performance for individual goals. However, in our experiments, we show that the average goal success rate improves and stabilizes. An agent trained with this method can be instructed to seek any observations made in the environment, enabling generic training of agents prior to specific use cases.