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MER works by first decomposing the long-horizon compositional tasks into several subtasks with individual goals according to the corresponding elements. Then, MER injects stochasticity into task-agnostic elements and trains goal-conditioned policies for different subtasks with injected noise.
Aug 3, 2023
Aug 3, 2023 · In this paper, we propose a more flexible method termed MER to learn a compositionally generalizable policy that depends on task-dependent invariant elements ...
Dec 31, 2022 · In this paper, we propose a more flexible method termed MER to learn a compositionally generalizable policy that depends on task-dependent ...
In this paper, we propose a more flexible method termed MER to learn a compositionally generalizable policy that depends on task-dependent invariant elements ...
MER: Modular Element Randomization for robust generalizable policy in deep reinforcement learning. https://doi.org/10.1016/j.knosys.2023.110613 ·. Journal ...
MER: Modular Element Randomization for robust generalizable policy in deep reinforcement learning. Knowledge-Based Systems. 2023-08 | Journal article.
MER: Modular Element Randomization for robust generalizable policy in deep reinforcement learning. Y Li, J Ren, T Zhang, Y Fang, F Chen. Knowledge-Based Systems ...
Apr 25, 2024 · MER: Modular Element Randomization for robust generalizable policy in deep reinforcement learning. Knowl. Based Syst. 273: 110613 (2023). [j6].
MER: Modular Element Randomization for robust generalizable policy in deep reinforcement learning. Y Li, J Ren, T Zhang, Y Fang, F Chen. Knowledge-Based ...
Dec 5, 2024 · Our algorithm GRAM achieves strong generalization performance across in-distribution and out-of-distribution scenarios upon deployment, which we ...
Missing: MER: Element Randomization generalizable