In this work, we take a representation learning perspective on hierarchical
reinforcement learning, where the problem of learning lower layers in a
hierarchy is transformed into the problem of learning trajectory-level
generative models. We show that we can learn continuous latent representations
of trajectories, which are effective in solving temporally extended and
multi-stage problems. Our proposed model, SeCTAR, draws inspiration from
variational autoencoders, and learns latent representations of trajectories. A
key component of this method is to learn both a latent-conditioned policy and a
latent-conditioned model which are consistent with each other. Given the same
latent, the policy generates a trajectory which should match the trajectory
predicted by the model. This model provides a built-in prediction mechanism, by
predicting the outcome of closed loop policy behavior. We propose a novel
algorithm for performing hierarchical RL with this model, combining model-based
planning in the learned latent space with an unsupervised exploration
objective. We show that our model is effective at reasoning over long horizons
with sparse rewards for several simulated tasks, outperforming standard
reinforcement learning methods and prior methods for hierarchical reasoning,
model-based planning, and exploration.

本文提出了一种用于连续性轨迹的生成式模型 ——SeCTAR，该模型结合了变分自编码器和深层强化学习的思想，并提出了一种用于学习 latent representations 的方法。通过在学习到的 latent space 中进行 model-based planning，该模型能够有效地完成多阶段的强化学习任务，超越了标准强化学习方法和以往用于层级推理、model-based planning、探索的方法。