如果你做强化学习，特别关注世界模型，这篇论文提出Reinformed Dreamer，用潜在引导训练不对称表示，比Informed Dreamer更稳定地超越Dreamer。
论文提出Reinformed Dreamer算法，通过潜在引导改进不对称表示学习，解决了Informed Dreamer在特权信息表示上的局限。在多个强化学习基准测试中，相比Dreamer基线，Reinformed Dreamer表现出更一致的性能提升。该方法利用训练期间的额外监督信息学习更好的观察和特权信息表示。
Reinformed Dreamer: An Asymmetric World Model Efficiently Trained through Latent Guidance
Much like humans benefit from guidance while learning, reinforcement learning algorithms may benefit from additional supervision beyond rewards. Leveraging additional information during training to learn better representations and behaviors has been the focus of asymmetric reinforcement learning. This learning paradigm has proven effective under partial observability when additional state information is available, but also under full observability when more refined state information is available. Focusing on model-based reinforcement learning, we study the effect of asymmetric learning on observation representations and on privileged information representations. First, we identify a limitation in the privileged information representations learned by an asymmetric model-based algorithm known as the Informed Dreamer. Then, we propose a novel asymmetric representation learning objective using latent guidance, resulting in a new algorithm called the Reinformed Dreamer. Experiments across several benchmarks show a more consistent improvement over Dreamer than previous asymmetric approaches.