Paper collection on building and evaluating language model agents via executable language grounding
large-language-modelsagentreinforcement-learningtool-usecode-generationweb-groundingcomplex-reasoninglanguage-agentllm-roboticsneural-symbolic- Markdown
01Обновлено 7 месяцев назад
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
machine-learningpytorchdeep-learningtensorflowmlopsjaxdata-sciencekerasml-platformmodel-versioningreinforcement-learningreproducibilitycollaborationdata-versioningexperiment-trackhyperparameter-optimizationhyperparameter-searchhyperparameter-tuning- Python
00Обновлено 7 месяцев назад
The Hierarchical Intrinsically Motivated Agent (HIMA) is an algorithm that is intended to exhibit an adaptive goal-directed behavior using neurophysiological models of the neocortex, basal ganglia, and thalamus.
reinforcement-learningbiologically-plausible-learninghierarchical-temporal-memoryintrinsic-motivationmodel-based-reinforcement-learningsparse-distributed-representations- Python
00Обновлено 2 месяца назад
POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings.
- Python
00Обновлено 2 месяца назад
[AAAI-2024] Follower: This study addresses the challenging problem of decentralized lifelong multi-agent pathfinding. The proposed Follower approach utilizes a combination of a planning algorithm for constructing a long-term plan and reinforcement learning for resolving local conflicts.
- C++
00Обновлено 2 месяца назад
"When to Switch" Implementation: Addressing the PO-MAPF challenge with RePlan & EPOM policies. This repo includes search-based re-planning, reinforcement learning techniques, and three mixed policies for pathfinding in partially observable multi-agent environments. 🤖🛤️
- Python
00Обновлено 2 месяца назад