DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement
  Learning
Anthony Liang Guy Tennenholtz Chih-wei Hsu Yinlam Chow Erdem Bıyık Craig Boutilier
We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to
approximate inference in environments where the latent state evolves at varying
rates. We model episode sessions - parts of the episode where the latent state
is fixed - and propose three key modifications to existing meta-RL methods:
consistency of latent information within sessions, session masking, and prior
latent conditioning. We demonstrate the importance of these modifications in
various domains, ranging from discrete Gridworld environments to
continuous-control and simulated robot assistive tasks, demonstrating that
DynaMITE-RL significantly outperforms state-of-the-art baselines in sample
efficiency and inference returns.