A significant bottleneck in applying current reinforcement learning
algorithms to real-world scenarios is the need to reset the environment between
every episode. This reset process demands substantial human intervention,
making it difficult for the agent to learn continuously and autonomously.
Several recent works have introduced autonomous reinforcement learning (ARL)
algorithms that generate curricula for jointly training reset and forward
policies. While their curricula can reduce the number of required manual resets
by taking into account the agent's learning progress, they rely on
task-specific knowledge, such as predefined initial states or reset reward
functions. In this paper, we propose a novel ARL algorithm that can generate a
curriculum adaptive to the agent's learning progress without task-specific
knowledge. Our curriculum empowers the agent to autonomously reset to diverse
and informative initial states. To achieve this, we introduce a success
discriminator that estimates the success probability from each initial state
when the agent follows the forward policy. The success discriminator is trained
with relabeled transitions in a self-supervised manner. Our experimental
results demonstrate that our ARL algorithm can generate an adaptive curriculum
and enable the agent to efficiently bootstrap to solve sparse-reward maze
navigation tasks, outperforming baselines with significantly fewer manual
resets.

提出了一种新颖的自主强化学习算法，能够根据智能体的学习进展生成自适应课程，使智能体能够高效地解决稀疏奖励迷宫导航任务，同时减少了手动复位。