DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning
<|reference_start|>DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning: 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.<|reference_end|>
@article{liang2024dynamite-rl:,
    title={DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement
  Learning},
    author={Anthony Liang, Guy Tennenholtz, Chih-wei Hsu, Yinlam Chow, Erdem
  B{i}y{i}k, Craig Boutilier},
    journal={arXiv preprint arXiv:2402.15957},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15957},
    primaryClass={cs.LG}
}