Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction
Masashi Okada,Tadahiro Taniguchi,Masashi Okada,Tadahiro Taniguchi
In the present paper, we propose a decoder-free extension of Dreamer, a leading model-based reinforcement learning (MBRL) method from pixels. Dreamer is a sample- and cost-efficient solution to robot learning, as it is used to train latent state-space models based on a variational autoencoder and to conduct policy optimization by latent trajectory imagination. However, this autoencoding based appr...
