Exploring every possibility and training a model for each is impractical.

China's annual gala on Lunar New Year's Eve has showcased Beijing's giant leap in technology as humanoid robots took centre stage to perform a joint martial arts routine featuring several firsts. China's Spring Festival Gala, which aired on Monday on state broadcaster CGTN, has gone viral, drawing nearly half a million views on YouTube. The performance marked a stark contrast with last year's show, when robots twirled handkerchiefs and performed simple movements. The first robots to appear were Noetix's Bumi models, who performed a comedy sketch. Unitree's robots later exhibited martial arts alongside child artists, including backflips and trampoline jumps, followed by Magiclab's humanoids in a musical segment.

Currently, two mainstream GNN-based methods have been developed for constructing force fields: group theory-based methods and direction-based methods.

However, it was recently shown that gradient inversion attacks can reconstruct this data from the shared gradients.

RL problems through the idea of "learning to learn". Current meta-RL methods can be classified in to two categories. These methods mainly differ in their ways of inference [3, 4, 20]. The other line follows the technique of relabeling that enables sample reuse across tasks, i.e., learning a task Packer et al. apply hindsight relabeling for meta-RL, and propose hindsight task relabeling (HTR) to relabel the trajectories Taking a step further than hindsight relabelling, Wan et al. introduce additionally foresight Huang et al. derive a general form of policy gradient from DR value estimator [29], whereas a DR off-policy actor-critic Kallus et al. propose the doubly robust method to find a robust policy that can Depending on the knowledge to be transferred, these methods in RL can be roughly divided into classes including sampled transitions [32, 33], learned policies or value networks [34, 35, 36, 37], features [38, 39, 40], and skills [41, 42]. Doubly Robust Property for Direct Use of Doubly Robust Estimator We show the doubly robust property of the DR estimator for value function in Eq. (5) in the main text, as follows.