Learning Task-Agnostic Skill Bases to Uncover Motor Primitives in Animal Behaviors

Animals flexibly recombine a finite set of core motor primitives to meet diverse task demands, but existing behavior-segmentation methods oversimplify this process by imposing discrete syllables under restrictive generative assumptions. To reflect the animal behavior generation procedure, we introduce skill-based imitation learning (SKIL) for behavior understanding, a reinforcement learning-based imitation framework that (1) infers interpretable skill sets, i.e., latent basis functions of behavior, by leveraging representation learning on transition probabilities, and (2) parameterizes policies as dynamic mixtures of these skills. We validate our approach on a simple grid world, a discrete labyrinth, and unconstrained videos of freely moving animals. Across tasks, it identifies reusable skill components, learns continuously evolving compositional policies, and generates realistic trajectories beyond the capabilities of traditional discrete models. By exploiting generative behavior modeling with compositional representations, our method offers a concise, principled account of how complex animal behaviors emerge from dynamic combinations of fundamental motor primitives.

Brain-Like Replay Naturally Emerges in Reinforcement Learning Agents

Can replay, as a widely observed neural activity pattern in brain regions, particularly in the hippocampus and neocortex, emerge in an artificial agent? If yes, does it contribute to the tasks? In this work, without heavy dependence on complex assumptions, we discover naturally emergent replay under task-optimized paradigm using a recurrent neural network-based reinforcement learning model, which mimics the hippocampus and prefrontal cortex, as well as their intercommunication and the sensory cortex input. The emergent replay in the hippocampus, which results from the episodic memory and cognitive map as well as environment observations, well resembles animal experimental data and serves as an effective indicator of high task performance. The model also successfully reproduces local and nonlocal replay, which matches the human experimental data. Our work provides a new avenue for understanding the mechanisms behind replay.