The lesser long-nosed bat (Leptonycteris yerbabuenae) is a medium-sized bat found in Central and North America. Breakthroughs, discoveries, and DIY tips sent six days a week. Biologists and engineers have joined forces to build a new robot bat that's helping us understand how bats use echolocation to hunt for food. By creating a robot that can echolocate, the team mimicked a bat's flight path and explained how bats can quickly determine whether or not their prey is on a leaf. This new bat's eye view is detailed in a study recently published in the The study was led in part by bat scientist and Smithsonian Tropical Research Institute research associate Inga Geipel .

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Communication lays the foundation for human cooperation.

'Aspidorhynchus' was an efficient predator-but sometimes they became the prey. Breakthroughs, discoveries, and DIY tips sent every weekday. An unusual type of fossilized fish can be found within the limestone of present-day Germany. The Late Jurassic era conditions exhibited at the famed Solnhofen deposits have preserved the remains of multiple marlin-like marine predators known as . But these carnivorous remains aren't complete specimens--they're decapitated heads still attached to gastrointestinal tracts.

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In addition, similar conclusions can be found in Ref.[24], "learning nearly decomposable value StarCraft II (cooperative game) and believed that this is because it cannot deal with the issue of reward assignment. Predators do not need to learn sophisticated strategy to encircle multiple moving preys. We will benchmark I2C alone and add it up for a more thorough comparison in the final version. If agents do not have any visibility radius, it means the environment is fully observable including all other agents. In addition, too much redundant information could impair the learning.

Dead units can only choose noop action.

It's critical to deal with the dynamic team composition in real-world multi-agent scenarios. MHA can easily achieve agents' partial observability with entity-wise expression. We commit our code in https://github.com/thu-rllab/SOG . 1 D Derivation of Message Summarizer For StarCraft Micromanagement Tasks, it takes about 20 hours to run one experiment. We summarize some hyper-parameters in Table. The radius of the home and the resource location & agent is 0.1 and 0.05, respectively.

The capacity of an embodied agent to understand, predict, and interact with its environment is fundamentally contingent on an internal world model. This paper introduces a novel framework for investigating the formation and adaptation of such world models within a biological substrate: human neural organoids. We present a curriculum of three scalable, closed-loop virtual environments designed to train these biological agents and probe the underlying synaptic mechanisms of learning, such as long-term potentiation (LTP) and long-term depression (LTD). We detail the design of three distinct task environments that demand progressively more sophisticated world models for successful decision-making: (1) a conditional avoidance task for learning static state-action contingencies, (2) a one-dimensional predator-prey scenario for goal-directed interaction, and (3) a replication of the classic Pong game for modeling dynamic, continuous-time systems. For each environment, we formalize the state and action spaces, the sensory encoding and motor decoding mechanisms, and the feedback protocols based on predictable (reward) and unpredictable (punishment) stimulation, which serve to drive model refinement. In a significant methodological advance, we propose a meta-learning approach where a Large Language Model automates the generative design and optimization of experimental protocols, thereby scaling the process of environment and curriculum design. Finally, we outline a multi-modal evaluation strategy that moves beyond task performance to directly measure the physical correlates of the learned world model by quantifying synaptic plasticity at electrophysiological, cellular, and molecular levels. This work bridges the gap between model-based reinforcement learning and computational neuroscience, offering a unique platform for studying embodiment, decision-making, and the physical basis of intelligence.

Environment Animals Wildlife Bats This tiny bat is one of the world's deadliest hunters Lions wish they killed this well. Breakthroughs, discoveries, and DIY tips sent every weekday. The lion is an undisputed contender for the planet's most iconic predator, but a new study indicates there is an underdog contender coming for its top spot. They're also often more successful at getting the job done. The proof is laid out in a study appropriately published on October 31 in the journal .