Search Results for "macromedia action 3.0"

VectorizedMultiAgentSimulator is a high-performance, vectorized simulator for multi-agent systems, focusing on large-scale agent interactions in shared environments. It is designed for research in multi-agent reinforcement learning, robotics, and autonomous systems where thousands of agents need to be simulated efficiently.

This Github Action automatically creates an animated GIF or WebP from a given web page to display on your project README (or anywhere else). In your GitHub repo, create a workflow file or extend an existing one. You have to also include a step to checkout and commit to the repo. You can use the following example gif.yml. Make sure to modify the url value and add any other input you want to use. WebP rendering will take a lot of time to benefit from lossless quality and file size...

Pyrogram is a modern, elegant and asynchronous MTProto API framework. It enables you to easily interact with the main Telegram API through a user account (custom client) or a bot identity (bot API alternative) using Python. Ready: Install Pyrogram with pip and start building your applications right away. Easy: Makes the Telegram API simple and intuitive, while still allowing advanced usages. Elegant: Low-level details are abstracted and re-presented in a more convenient way. Fast: Boosted up...

A library for implementing GOAP in an AI agent. Based on pygoap v3 by Leif Theden et al. Updated code to work without having pygame installed, bug-fixed functions to make them implement the behaviors that were expected, and implemented desired behaviors so that the Pirate demo works properly for all known actions and goals.

A proximity forest is a data structure that allows for efficient computation of approximate nearest neighbors of arbitrary data elements in a metric space. See: O'Hara and Draper, "Are You Using the Right Approximate Nearest Neighbor Algorithm?", WACV 2013 (best student paper award). One application of a ProximityForest is given in the following CVPR publication: Stephen O'Hara and Bruce A. Draper, "Scalable Action Recognition with a Subspace Forest," IEEE Conference on Computer...